Autumn 2020 Volume I, Issue I skyshotonline.com
Astronomy Made Accessible
The History and Potentially Imminent Explosion of Betelgeuse p. 10 Research: Analysis of Supernova SN 2019hyk p. 12
Computational Astrophysics Advancements of 2020 p. 39 Advancements in Aerospace p. 44
Comet NEOWISE Recap and Astrophotography
Astrophotography on the Cheap: A Guide
“method of gauss” “starry dreams”
Letter from the Editing Team On behalf of the editing team, welcome to the first publication of SkyShot! Our goal with this magazine is to launch a platform where students can share their spacerelated works with a wide audience. In this inaugural issue, we are excited to spotlight some great work that our contributors have created, with a wide range of topics covering research, articles, poetry, astrophotography, and more. We sincerely thank all contributors for sharing their passion and hard work with us. We hope you enjoy this seasonâ&#x20AC;&#x2122;s publication, and we look forward to growing this platform with all of you! Happy reading! The SkyShot Team
Editing Team Founder: Priti Rangnekar President: Rutvik Marathe Executive Directors: Naunet Leonhardes-Barboza Victoria Lu Carter Moyer Vighnesh Nagpal Ezgi Zeren Head of Astrophysics: Andrew Tran Head of Content Management: Anavi Uppal Head of Peer Review: Alexandra Masegian Peer Reviewer: Feli X
Front and back cover photo: Rosette Nebula (NGC 2244) ÂŠ Jonah Rolfness
Founder’s Note Against all odds amidst a pandemic, 2020 has been a remarkable year for astronomy, astrophysics, and aerospace alike. As the first crewed launch from U.S. soil in nearly a decade, the Crew Dragon Demo-2 ushered in high spirits. Around the world, astronomers embarked on a quest to capture the magnificence of Comet NEOWISE. During the autumn, three phenomenal scientists earned the Nobel Prize in Physics for their research in black holes and cosmology. Time and time again, the space sciences prove their unique worth as a force for uniting humanity. Simultaneously, I was inspired by the sheer diversity of space-related endeavors pursued by our young generation. As my exoplanet research group pored over online transit data, the essence of teamwork transcended boundaries of time zones or location. I saw sparks of joy whenever fellow Summer Science Program alumni discussed advice about imaging nebulas or shared their spaceinspired arts. It was clear that students deserved a platform that showcased their efforts in space science while serving as a welcoming, nurturing community. An initiative consisting of five projects, “Science Connect” was founded with the mission of amplifying multidisciplinary education through opportunities in hands-on problem-solving and communication. SkyShot became our flagship project as a stunning paragon of collaboration and fusion of disciplines. After all, the universe is not based upon arbitrary divisions in academic fields - it simply exists, and we strive towards appreciation and understanding. Outer space presents an unparalleled juxtaposition of “science” and “art.” On one hand, the night skies inspire humility and awe at the vast expanse above. On the other, we are compelled to ask fundamental questions regarding astrophysical processes and embrace the final frontier through engineering advances. Above all, our innate curiosity and connection with outer space drive humankind to reach its full potential. As you read SkyShot, immerse yourself in the multifaceted nature of the cosmos. Engaging with the wonders of outer space is for everyone, regardless of demographics, background, or academic interest. I hope you can find and reflect upon your unique avenues of doing so. Together, we will launch into a new era of unbridled intellectual growth and exploration. - Priti Rangnekar 3
p. 46 p. 48 4
In This Issue
6 Astronomical Sightseeing in 2020 Abby Kinney
8 Comet NEOWISE and NEOWISE Astrophotography Victoria Lu, Anavi Uppal, and Owen Mitchell
10 The History and Potentially Imminent Explosion of Betelgeuse
12 Photometric and Spectroscopic Analysis of the Type II-P Supernova SN 2019hyk
Sofia Fausone, Timothy Francis Hein, Anavi Uppal, and Zihang Wang
16 On Methods of Discovering Exoplanets
20 Astrophotography on the Cheap: A Guide
22 Astrophotography Gallery
Ryan Caputo, Owen Mitchell, Jonah Rolfness, Nathan Sunbury, Anavi Uppal, Cameron Woo, Wilson Zheng
28 Removing Noise and Improving the Quality of Astronomical Observations with Fourier Transforms
39 Computational Astrophysics Advancements of 2020
44 Advancements in Aerospace
46 Access to Space
48 Understanding the Chronology of the Universe
51 2002 KM6 (99795)
51 method of gauss
52 tessellated constellations
53 starry dreams
54 images of the past
55 unseen skies
56 Astrophotography Index 59 Educational Opportunities 60Contributor Biographies
Astronomical Sightseeing in 2020 Abby Kinney
Mars at its Best This autumn has been a great time to view Mars. On October 13th, the planet reached opposition, meaning it appeared opposite the Sun in the sky, reaching its highest point in the sky at midnight. However, Mars was a spectacular sight earlier this season as well. Mars dramatically increased brightness, eventually surpassing Jupiter’s brightness on September 24th, and reached a magnitude of -2.6 at opposition. Additionally, the apparent size of Mars’ disk was approximately 22 arcseconds . Perhaps you’re reading this in disappointment remembering the last Mars opposition in 2018 when Mars reached a magnitude of -2.8 and disk size of about 24 arcseconds . But for Northern Hemisphere observers this 2020 opposition had something else going for it: Mars’s altitude in the sky was much higher. In 2018, Mars appeared in the constellation of Capricornus. But this year, Mars appeared in Pisces, which is further North in the sky . Whether you were viewing the red planet with your eyes, binoculars, or telescopes, it fulfilled its promise of being a breathtaking fall sight.
Source: EarthSky Community Photos. Citations  Dickinson, David. “Enter the Red Planet: Our Guide to Mars Opposition 2018.” Universe Today, 18 July 2018, www.universetoday.com/139420/enter-the-red-planet-our-guide-to-marsopposition-2018/.  Lawrence, Pete. “Mars Opposition 2020: Get Ready to Observe the Red Planet.” Skyatnightmagazine, 24 Aug. 2020, www. skyatnightmagazine.com/advice/skills/how-to-observe-mars/.  Pasachoff, Jay M. Stars and Planets. Houghton Mifflin, 2000.
Halloween’s Blue Moon Everyone’s heard the phrase “once in a blue moon,” but this Halloween, we had a chance to see such a moon. However, the term “blue moon” is actually quite misleading. First, blue moons are not necessarily blue. Rather than indicating a color, a “blue” moon usually refers to the second full moon of a calendar month . But this now-common definition was actually a mistake created by the misinterpretation of the Maine Farmer’s Almanac in a 1946 Sky and Telescope article titled “Once in a Blue Moon.” The mistake was then popularized by a reading of this article on the radio program StarDate. Eventually, this new definition even found its way into the popular game Trivial Pursuit . The Maine Farmer’s Almanac defined a “blue moon” as the third full moon of a season with four full moons. This may seem like a distinction without a difference, but if we look at this Halloween blue moon, it was actually the second full moon of Autumn. Thus, by the almanac’s definition, it would not be a full moon. In fact, the next seasonal blue moon is in August of 2021, despite that month having only one full moon. In addition to lacking their eponymous blue hue, blue moons are not as uncommon as the ubiquitous phrase would suggest. Because most calendar months are longer than the moon’s approximately 29.5 day synodic period (the period of its phases), a blue moon can occur if the first full moon was sufficiently early in the month. Thus, a blue moon typically happens every two to three years and will always occur on either the 30th or 31st . While blue moons in general are fairly common, a blue moon on Halloween is more special: the last blue moon to fall on October 31st was in 1974, and it won’t happen again until 2039 . In light of the surprising frequency of blue moons, you may be wondering how the common refrain “once in a blue moon” came to indicate something rarely occurring; however, the refrain actually predates our modern usage. It was used to indicate something that was as absurd as the Moon turning blue. Over time, the phrase came to mean something similar to never or very rarely . But the moon can actually appear bluish. While the light from the moon is reflected from the Sun and, therefore, virtually identical to daylight, atmospheric conditions can change the appearance of the moon . For instance, after the eruption of the volcano Krakatoa in 1883, volcanic ash in the air was the perfect size to scatter the redder colors of the moonlight, giving the moon a bluer tint . So whether you were handing out candy or trick-ortreating yourself this year, that not-so-blue blue moon was definitely worth looking at.
space/everything-you-need-to-know-geminid-meteor-shower.  Sky Events Calendar by Fred Espenak and Sumit Dutta
The Great Conjunction of 2020 The full moon on October 31, 2020, Hungary. Source: USA Today. Citations  Dunbar, Brian. “Blue Moon.” NASA, NASA, 7 July 2004, www.nasa.gov/vision/universe/watchtheskies/07jul_bluemoon.html.  Hiscock, Philip. “Blue Moons - Origins and History of the Phrase.” Sky & Telescope, 20 Apr. 2020, skyandtelescope.org/ observing/celestial-objects-to-watch/once-in-a-blue-moon/.  O’Meara, Stephen James. “The Blue Moons.” Astronomy.com, 12 Sept. 2016, astronomy.com/magazine/stephen-omeara/2016/09/the-blue-moons.  Sky Events Calendar by Fred Espenak and Sumit Dutta (NASA’s GSFC)
A Moonless Meteor Shower Every year, around mid-December, stargazers are treated to the Geminid Meteor Shower. This meteor shower is thought to be caused by the Earth crossing a swath of small particles leftover from comet 3200 Phaethon. The particles entering the atmosphere comprise the wonderful meteor shower that we witness each year . This year, however, is a special experience, because the maximum of the 2020 Geminid Meteor Shower falls on December 14th, which nearly coincides with the new moon. This means that there will be no moon in the night sky to interfere with seeing the fainter meteors. During the maximum, 60 to 120 meteors may be seen each hour. As a general rule, the higher the two bright stars, Castor and Pollux of Gemini, are in the sky, the more meteors will be seen. This is because these stars in Gemini are near the radiant of the Geminid Meteor Shower, which is the point in the sky that appears to be the origin of the meteors . This year’s moonless Geminid Meteor Shower has the potential to be a spectacular way to spend a December night. So get outside and look up! Citations  Pasachoff, Jay M. Stars and Planets. Houghton Mifflin, 2000.  McClure, Bruce, and Deborah Byrd. “This Weekend’s Geminid Meteor Shower.” EarthSky, 2019, earthsky.org/
All summer long, the two largest planets of our solar system, Jupiter and Saturn, have shared the evening sky. But on December 21st, the two planets will reach conjunction, meaning they will share the same right ascension. While the actual time of conjunction will occur at 13:22 UTC, the planets will appear very close all night and can be observed getting closer over the course of several days leading up to their conjunction. But at their closest, they will be separated by only 6 arcminutes, roughly a fifth the apparent diameter of a full moon. Conjunctions of Jupiter and Saturn are not rare, per se: they occur approximately every 20 years due to the difference in the orbital periods of Saturn and Jupiter. Saturn, being farther from the Sun, takes about 30 years to orbit, while Jupiter takes about 12 years. This means that Jupiter travels about 30 degrees in the sky every year, while Saturn travels only 12 degrees . This difference in rates means every year Jupiter gains about 18 degrees on Saturn. Consequently, it takes approximately 20 years for Jupiter to “lap” Saturn. Thus, there are approximately 20 years in between conjunctions of Saturn and Jupiter. However, this year’s conjunction is truly special: Jupiter and Saturn have not appeared so close in the sky since 1623 and will not again until 2080. With the naked eye, Jupiter and Saturn may appear to be one point of light, but with just a small pair of binoculars they can be resolved into two small disks with Jupiter being the brighter of the two. With a telescope, many more features can be seen, including cloud bands on Jupiter, the rings of Saturn, Jupiter’s Galilean moons (Io, Europa, Callisto, and Ganymede), and Saturn’s largest moon, Titan . The location of Jupiter and Saturn low in the southwestern sky may make viewing difficult, especially if there are trees or other obstructions in that region. Additionally, the planets will only be visible for a short time after sunset . In spite of these challenges, this year’s great conjunction is an historic one and the meeting of these two majestic planets will be a sight worth seeking out. Citations  Money, Paul. “Upcoming Conjunctions in the Night Sky, and How to See Them.” Skyatnightmagazine, 26 Aug. 2020, www.skyatnightmagazine.com/advice/skills/conjunctions-in-night-sky-how-see/.  Etz, Donald V. “Conjunction of Jupiter and Saturn.” Journal of the Royal Astronomical Society of Canada, vol. 94, Aug. 2000, pp. 174–178.  Pasachoff, Jay M. Stars and Planets. Houghton Mifflin, 2000.
Comet NEOWISE Victoria Lu July of 2020 was a notable month for stargazers as Comet NEOWISE appeared in night skies. The comet was the brightest to appear in the Northern Hemisphere in decades and was visible to the naked eye. Comet NEOWISE was discovered on March 27, 2020 by astronomers using NASA’s Wide-field Infrared Survey Explorer (WISE) telescope. WISE launched in 2009 and utilizes infrared wavelengths to search for galaxies, cold stars, and near-earth objects such as asteroids and comets. The telescope’s infrared channels detected the heat signatures of the comet. From its signature, scientists determined that the comet was about 5 kilometers across and covered with sooty particles leftover from the birth of the solar system . Comets are balls of frozen gas, dust, and rock that orbit the sun . When a comet such as NEOWISE nears the sun, the increased heat forms a coma around the nucleus. The coma— which can be considered an atmosphere composed of particles and gases—is blown to form a tail. Comet NEOWISE has been observed to have both an ion tail and a dust tail. A dust tail forms when dust within the nucleus is forced out by solar radiation pressure. An ion tail, on the other hand, is birthed when ultraviolet radiation forces electrons to eject from the coma. The particles ionize and form a plasma, which interacts with high solar winds to form a tail. Such tails can stretch for millions of miles . NEOWISE was not considered a “great comet” (an exceptionally brilliant comet with a long tail), but it dazzled viewers nonetheless. Its brightness enabled viewers to observe it with the naked eye in dark locations, or with the assistance of binoculars and telescopes . Comet NEOWISE’s proximity to the Big Dipper made it easier for inexperienced viewers to spot. NEOWISE was difficult to see in areas with high light pollution but was still visible after some persistence. On July 22, the comet reached perigee, passing as close to the Earth as it would come at a distance of 103.5 million km. Comet NEOWISE had been fading since it reached perihelion (the closest point to the sun) on July 3rd, but its approach to Earth made the dimming less noticeable. After reaching perigee, however, the comet steadily dimmed as it grew further from Earth. On July 4th the comet had a magnitude of +1.6, but in subsequent weeks it dimmed, becoming an 8th magnitude object towards the end of mid-August. Magnitude is a measure of brightness, with higher numbers signifying dimmer objects . Once gone, this comet will not appear for another 6,800 years. One fact is for certain—Comet NEOWISE put on a dazzling display for viewers worldwide before bidding goodbye. 8
Citations  Furfaro, E. (2020, July 14). How to See Comet NEOWISE. Retrieved August 25, 2020, from https:// www.nasa.gov/feature/how-to-see-comet-neowise  Hartono, N. (2020, July 08). Comet NEOWISE Sizzles as It Slides by the Sun. Retrieved August 25, 2020, from https://www.nasa.gov/feature/jpl/cometneowise-sizzles-as-it-slides-by-the-sun-providing-atreat-for-observers  Isaacs-Thomas, I. (2020, July 28). How to spot Comet NEOWISE before it disappears for thousands of years. Retrieved August 25, 2020, from https:// www.pbs.org/newshour/science/how-to-spot-comet-neowise-before-it-disappears-for-thousands-ofyears  NASA. (n.d.). Comets. Retrieved August 25, 2020, from https://solarsystem.nasa.gov/asteroids-comets-and-meteors/comets/overview/?page=0  Rao, J. (2020, July 24). The curtain is about to come down on Comet NEOWISE. Retrieved August 25, 2020, from https://www.space.com/comet-neowise-is-dimming.html
Comet NEOWISE, as observed in mid-July from Bozeman, Montana. © Owen Mitchell
Comet NEOWISE (Zoomed), as observed on July 19 in Orlando, Florida. © Anavi Uppal
Comet NEOWISE (wide), as observed on July 19 in Orlando, Florida. © Anavi Uppal
The History and Potentially Imminent Explosion of Betelgeuse Alexandra Masegian
The changing surface of fading Betelgeuse. Source: NASA, 2020. Sitting high in the left shoulder of the Orion constellation, the massive star known as Betelgeuse is nearing the end of its life. As one of the largest and most luminous stars in the sky, Betelgeuse has been identified as an M1-2 type red supergiant star . When it dies, it will undergo a catastrophic process known as a supernova, flinging its outer layers into space in a massive explosion and leaving behind a core so dense that it will likely become a black hole. The only question is when that supernova will occur — a question that, over the course of the past year, astrophysicists thought was on the verge of being answered. Betelgeuse is known to be a semi-regular variable star, meaning that its brightness fluctuates periodically on multiple timescales. Because it is so bright, these fluctuations are often visible to the naked eye and were noticed even by the earliest of astronomers. In fact, the first recorded instance of Betelgeuse’s variability dates back to 1836. The event was described in Sir John Herschel’s 1849 Outlines of Astronomy, who wrote, “The variations of Alpha Orionis, which were most striking and unequivocal in the years 1836-1840, within the years since elapsed became much less conspicuous” . By 10
piecing together observations like Herschel’s and more modern data, astrophysicists have deduced that Betelgeuse primarily pulsates on a timescale of almost 425 days, with secondary periods of 100-180 days and 2,153 days (5.9 years) . This means that slight fluctuations in the star’s brightness are both commonplace and expected — within the confines of the expected pattern, of course. Over the course of the past year, however, the massive star’s brightness has been fluctuating in ways that defy this usual pattern. In October of 2019, Betelgeuse began to dim at a point in its cycle where it normally would have been bright. Though the change was not noticeable at first, by December of 2019 the supergiant had lost over two-thirds of its usual brilliance, enough to reduce it from one of the top ten brightest stars in the sky to the twenty-first.  The star’s dimming was the most severe since precise monitoring of its atmosphere began nearly a century ago, and it lasted much longer than would be normal for the star’s typical cycle. (If the dimming was just a product of two of the star’s cycles overlapping at their minimum points, for instance, it would have only lasted a few weeks rather than several months.) Astronomers around
the world began to take notice. Was Betelgeuse on the verge of supernova? Little is known about how massive stars behave in the years leading up to their explosive deaths. Though historical evidence indicates that humanity has witnessed supernovae before, the last such event to occur in our galaxy was in 1604, long before the advent of modern telescopes and observing technology.  Even though we have never been able to observe a star during its final moments before the supernova, astrophysicists have made predictions as to what some of the early warning signs could be. One of those possible signs is what astrophysicist Sarafina Nance calls “insane and violent mass loss.”  In theory, a dying star will shoot a large portion of its mass into space right before its death, which could cause the star to appear dimmer as clouds of ejected dust block its light from reaching the Earth. Betelgeuse’s sudden and significant dimming, therefore, seemed to be a sign that the red supergiant was in the throes of death. As astronomers scrambled to develop theories for what could be causing the star’s abrupt change, Harvard-Smithsonian astrophysicist Andrea Dupree and her team turned to the Hubble Space Telescope, which they started using to monitor Betelgeuse in early January of 2019. They were able to isolate several months of ultraviolet-light spectroscopic observations of the star in the time leading up to its dimming, and analysis of the data revealed signs of dense, hot material moving through the star’s atmosphere in the months of September, October, and November 2019.  Traveling at nearly 200,000 miles per hour, the material continued beyond Betelgeuse’s visible surface and out into space at around the same time that the star underwent its most significant dimming. Dupree theorizes that once the material had separated from the incredibly hot stellar surface, it was able to cool enough to form a large gas cloud, which would have blocked a large portion of Betelgeuse’s light and made it appear much dimmer to us here on Earth.  Meanwhile, the star’s normal pulsational cycle continued as usual, the behavior of
the star’s outer atmosphere returning to normal even as the dust lingered. It is not yet understood what caused the stellar outburst, though Dupree and her colleague Klaus Strassmeier of the Leibniz Institute for Astrophysics in Potsdam think that it may have been a result of the star expanding in its pulsation cycle at the same time that a pocket of material underneath the star’s surface was experiencing an upwelling. The surge of force accompanying Betelgeuse’s expansion could have propelled the hot plasma in the convection cell outward, providing it with enough momentum to escape the star’s atmosphere. Though the star has since stabilized and seems to have returned to its normal pulsational cycle and brightness, the mass ejection that Dupree observed has been found to contain nearly twice the amount of material that is typical of a Betelgeuse outburst.  The question of whether or not it is on the brink of supernova remains an open one. When Betelgeuse eventually does reach the end of its life, the resulting explosion will be bright enough to be visible during the day and cast shadows at night here on Earth. Though our planet is far enough away from the massive star to avoid the majority of the radiation that the supernova will produce, the afterimage of the supergiant’s death will linger in the sky for months, serving as a stark reminder of the vastness and beauty of our universe. The star’s odd behavior this past year might not be the signal we’re looking for to indicate Betelgeuse’s imminent death, but it certainly is a sign that the star is growing more unstable — and, whenever it does finally explode, its death will undoubtedly be one of the most exciting astrophysical events of the millennium.
org/vsots_alphaori>  King, Bob. (2019 December 21). Betelgeuse is Dimming… Why? Sky & Telescope. <https://skyandtelescope.org/observing/fainting-betelgeuse/>  Vink J. (2016) “Supernova 1604, Kepler’s Supernova, and Its Remnant.” In: Alsabti A., Murdin P. (eds) Handbook of Supernovae. Springer, Cham. DOI:10.1007/978-3-319-20794-0_49-1  Drake, Nadia. (2019 December 26). A giant star is acting strange, and astronomers are buzzing. National Geographic. <https://www.nationalgeographic. com/science/2019/12/betelgeuse-is-acting-strange-astronomers-are-buzzing-about-supernova/>  Dupree, Andrea et al. (2020). “Spatially Resolved Ultraviolet Spectroscopy of the Great Dimming of Betelgeuse.” The Astrophysical Journal. 899:1. DOI: 10.3847/1538-4357/aba516
References  Keenan, Philip C.; McNeil, Raymond C. (1989). “The Perkins catalog of revised MK types for the cooler stars”. Astrophysical Journal Supplement Series. 71:245. DOI: 10.1086/191373.  Davis, Kate. Alpha Orionis (Betelgeuse). American Association of Variable Star Observers. <https://www.aavso. 11
Photometric and Spectroscopic Analysis of the Type II-P Supernova SN 2019hyk Sofia Fausone Healdsburg High School Timothy Francis Hein Los Altos High School Anavi Uppal Windermere Preparatory School Zihang Wang Webb School of California Dated: August 2, 2019 Abstract Over a period of four weeks, we performed spectroscopy and BVRI color photometry on SN 2019hyk, a supernova located in galaxy IC 4397. Using the telescopes at Yale University’s Leitner Family Observatory and on iTelescope.net, we took images throughout July 2019 to generate a light curve of the supernova. After evaluating the shape of the light curve and the emission spectrum, we conclude that SN 2019hyk is a type II-P supernova.
The discovery, observation, and analysis of supernovae provide us with valuable insight into the processes and physics behind these events. Supernovae are largely responsible for distributing heavy metals throughout space, and are used as standard candles for measuring cosmological distances. Investigations into supernovae not only deepen our understanding of stellar physics, but also reveal information about the broader structure of the universe. SN 2019hyk is located in the galaxy IC 4397, a type Sbc galaxy with an absolute magnitude of 13.2 . Discovered in 1889 in the Bootes constellation, the galaxy is about 203 million light years away from Earth . We used both spectroscopy and photometry to study the supernova. The former provides information on emission and absorption lines, and the latter provides instrumental magnitudes. After color-correcting instrumental magnitudes to apparent magnitudes, we created a light curve which provides information on the supernova’s classification when compared to models of known supernovae. The current system for the classification of supernovae was established in 1940 by Fritz Zwicky and Walter Baade
in the 1940s, and it is beginning to show signs of old age. The discovery of superluminous supernovae (SLSN) and their awkward subdivisions under Zwicky and Baade’s classification system show that this system is outdated. The different classifications of supernovae do not have clear relations with each other. In order to remake the current supernova classification system, it is imperative that we study more supernovae . To this end, our team decided to study SN 2019hyk.
Figure 1: SN 2019hyk
II. Methodology A. Photometry and Observations The photometric analysis of SN 2019hyk consists of three steps: telescopic observing, raw image processing, and leastsquares fitting color calibration. We took images with the Leitner Family Observatory’s 16-inch Ritchey-Chretien Telescope, as well as remote telescopes from iTelescope.net. We used a STL1001E CCD camera with the 16 inch telescope for all our data except on our last day of observing, when we used the STT1603ME CCD camera. To slew the telescope, calibrate, focus, and take images, we used the SkyX program. Arcturus was our pointing calibration star throughout the observations, and we used Tpoint pointing models, with at least two stars, each session. After checking the position of Arcturus and making a new pointing model if needed, we slewed to the supernova. Here, we checked that the star field was correct and the telescope focused. If we needed to adjust the focus, we used the RCOS TCC control app to move the secondary mirror. We took series of images using Johnson V and Johnson R filters with 1x1 binning and exposure times of 120 seconds each. We also used remote telescope T21 in New Mexico to take images of SN 2019hyk in Johnson V and Johnson R filters with 2x2 binning, for exposure times of 120 seconds. We processed our images using the software MaxIm DL. We first flat-fielded our images, and then aligned our images using auto star matching with bicubic resampling enabled. We then median combined the images taken in the same filter and adjusted their light histograms in order to make SN 2019hyk appear with the greatest resolution and contrast. Using software from Astrometry.net, we plate-solved our combined V and R filter images to acquire sky coordinates. Considering that the supernova is visibly distinct from its galaxy, and little light from the galaxy interferes with the light captured by our supernova apertures, we elected to not perform galaxy subtraction. Doing so would vertically shift our light curve, likely by a marginal amount, and not affect the shape or size of the curve.
Figure 2: Observation Schedule
B. Photometric Analysis We programmed an automated pipeline that extracts useful information from the combined images to calculate the standard magnitude of SN 2019hyk. The program first measures the flux of the supernova by summing up the pixel values passing through a 14-pixel circular aperture, and subtracting the average background noise collected by a 20-pixel annulus. The flux of the supernova is related to its instrumental magnitude through:
where m is the instrumental magnitude, and b is the flux. Using this relationship, we can calculate the instrumental magnitude of the supernova in each combined image. However, to acquire the supernova’s standard magnitude, we must also perform a color calibration on the instrumental magnitudes. After a cross-examination of the supernova’s magnitudes in both V filter and R filter, we can correct for the linear shift in magnitudes caused by the CCD’s color biases. To perform a linear transform on our instrumental magnitudes, we need the instrumental magnitudes of the supernova in both V and R filters. After comparing them to the standard magnitudes, we perform two least-squares fittings to acquire the transformation coefficients:
where v and r are instrumental magnitudes, V and R are standard magnitudes, and Tvr,Cvr,Tv and Cv are the coefficients we are interested in. The pipeline was fed with the coordinates, as well as V and R standard magnitudes of 30 calibration stars from APASS (The AAVSO Photometric All-Sky Survey), as shown in Figure 3. These calibration stars provide a general correlation between color and instrumental magnitude. The pipeline locates these calibration stars using the WCS files of solve-fielded images and then measures their instrumental magnitudes with the same aperture and annulus. It then uses least-squares fitting to calculate the values of transformation coefficients by making a V - R vs v - r plot and a V - v vs V - R plot. The color calibration coefficients enabled us to derive the standard magnitude of SN 2019hyk and generate a light curve, upon which we could determine the supernova’s type through a model fitting.
III. Results The instrumental magnitudes v and r, color-calibrated apparent magnitudes V and R, and errors of V are included in the table below. Figure 4: Instrumental and Apparent Magnitudes
Figure 3: 30 APASS Calibration Stars in the Vicinity of SN 2019hyk C. Spectroscopy We took spectra of the supernova using a DSS7 spectrometer on the 20-inch reflector at the Hotchkiss School to perform spectroscopy. To minimize background noises and light pollution, we subtracted the sky image from the supernova image. We then performed a linear fit on a calibration spectrum of Arcturus to an archive spectrum to derive the corresponding wavelengths. Finally, we transformed the spectrum of the supernova using the redshift of its host galaxy. Using the formula:
Figure 5 is the color-calibrated light curve for SN 2019hyk in V band. Six apparent magnitudes are plotted against days since peak magnitude, which occurred on June 27, 2019, according to our model. With a steady plateau, the light curve clearly matches the model of a Type II-P supernova despite the relatively large uncertainties in the second and last measurements. The most recent observations indicate that the supernova will continue to dim in the following month.
This derives a plot of our spectrum that indicates the supernova’s composition. D. Error Propagation We created covariance matrices for each filter’s linear fit process to calculate the uncertainty of our measurements of SN 2019hyk’s V and R magnitudes. We then diagonalized each matrix and took their square roots. From this process, we obtained two values for each linear fit: the slope uncertainty and the offset uncertainty. We then plugged these uncertainties into our linear fit line equations in order to find the maximum and minimum V and R magnitudes possible with those uncertainties. These minimum and maximum magnitudes formed the endpoints of our error bars on our SN 2019hyk error curve. As the error involved in measuring the flux of SN 2019hyk and our calibration stars was negligible, we decided not to include it in our uncertainty calculations.
Figure 5: V Band Light Curve with Type II-P SuperFigure 6 shows both the V and R light curve of SN 2019hyk. The R magnitudes are generally brighter than the V magnitudes, with the exception of the second and last measurements. Just like most supernovae, R magnitude changes following the trend of V magnitude. Figure 7 is a spectrum of SN 2019hyk taken by the 20inch reflector at the Hotchkiss School. Due to the limited aperture size, exposure time, and the supernova’s decreas-
ing brightness, there is a lot of noise in the spectrum, even after sky subtraction. However, it can still be clearly seen that the spectrum peaks at 656nm, the wavelength of the Hα emission line. This spectral identity corresponds to the photometric measurements and further proves SN 2019hyk to be a Type II supernova.
IV. Conclusions Our research on SN 2019hyk contributes to a more complete understanding of supernovae and their extragalactic significance. More specifically, we can use the spectrum and light curve to identify aspects of the origin and composition of SN 2019hyk. SN 2019hyk is a type II-P supernova. This is evident from both the Hα emission line in its spectrum and the prolonged period of high luminosity ‘plateau’ following this peak. SN 2019hyk is the result of the collapse of a giant star, roughly eight to fifty times as massive as our sun. Such a star would have passed the silicon burning process and released abundant heavy elements as it went supernova, creating the cradle for newborn stars and planets.
Figure 6: R Band Light Curve with Type II-P Supernova
Going forward, we would like to continue taking V and R images of SN 2019hyk using remote telescopes to prolong our light curve. If possible, we would also like to take B images of SN 2019hyk in order to obtain more accurate magnitude information. Later on, we will move on to other newly-discovered supernovae and study their spectra and light curves. Our data, along with the supernova data from observers worldwide, will help astronomers to gain a better understanding of supernova physics and the star forming process. Acknowledgements Thank you to the following people for assisting us with our research on SN 2019hyk: Dr. Michael Faison, Michael Warrener, Ava Polzin, Trustin Henderson, and Imad Pasha. References
Figure 7: Spectrum of SN 2019hyk with Hα Emission Line
 Stanek, K.~Z. 2019, Transient Name Server Discovery Report 2019-1053  Seligman, 2019, Index Catalog Objects: IC 4350 4399  Virtual Telescope, 2019, Supernova SN 2019hyk in the Spiral Galaxy IC-4397  Weizmann, 2019, SN 2019hyk Transient Name Server  Stevenson D. S, 2014, Extreme Explosions Supernovae, Hypernovae, Magnetars, and Other Unusual Cosmic Blasts, Springer, New York
Several of our images were taken on days with poor seeing and light cloud cover, which increased the noise in our images. The images taken on July 14th are poorly focused. Additionally, our images from July 27th were taken with the STT1603ME CCD camera instead of our usual STL1001E CCD camera. For extraneous reasons, we could not take flats for these images. This further increased the noise present in our images and increased our uncertainties.
On Method Exo
ds of Discovering oplanets
17 Artist concept of the TRAPPIST-1 planetary system. Source: NASA, 2018
We are currently in the midst of a golden age of planet discovery, a burst that has come along quite rapidly. Even as little as twenty-five years ago, the question of whether there existed planets orbiting other stars like our Sun remained open. However, in 1995, a team led by Michel Mayor and Didier Queloz at the University of Geneva achieved the first definitive detection of an exoplanet in orbit around a main-sequence star: 51 Pegasi B. In the years since, exoplanet science has come leaps and bounds. Through the efforts of scientists worldwide and large-scale endeavours such as the Kepler Space Telescope, we now know of over 4000 confirmed exoplanets in more than 3000 unique stellar systems—and we’ve barely scratched the surface. But how on Earth do we find these planets? Today, many methods for discovering exoplanets exist, of which two have experienced the most success: transits and radial velocities. The first of these, the transit method, works by monitoring the brightness of stars over time. A planet passing between us and a star will block a small fraction of its star’s light, which will manifest a dip in the star’s brightness as we observe it. Observing such dips in a star’s lightcurve is a telltale piece of evidence for the presence of planets orbiting it. The transit method is most sensitive to short period planets with large radii, since we are more likely to both catch such planets in a transit and detect the resultant dip in the star’s lightcurve. Despite this method’s limitations with regards to detecting longer period planets, missions such as the Kepler Space Telescope have phenomenally exploited this technique. Kepler alone managed to find
2662 exoplanets, more than half of all those currently known! The second of these techniques is called the radial velocity method. Whenever two bodies form a gravitationally bound system, each settles into orbit around their common centre of mass (COM). In most planetary systems, the host star’s mass far outweighs that of any planets that may orbit it, causing the COM’s location to lie very close to the star. In our case, the COM of the Earth-Sun system lies within the Sun’s outer layers! As a consequence, our orbital motion around the COM is much more apparent than the Sun’s, which is perhaps best described as a periodic wobble. While such wobbles are invisible to the naked eye and usually too small to cause a perceptible difference in a star’s position on an image, we can detect them by examining a star’s spectrum. Applying the Doppler Effect to spectral lines (Figure 1) makes it possible to measure the star’s radial velocity, which is the component of a star’s velocity pointing directly along our line of sight. Periodic trends in a star’s radial velocity (RV) can indicate the presence of an additional object—whether that be a planet or stellar companion—that causes the observed motion. These trends allow us to, through their gravitational influence, detect planets or faint stellar companions. It is then possible to use the data contained within the radial velocity trend to investigate the nature of the companion’s orbit—a procedure known as orbit fitting. Orbit fitting using radial velocities can provide useful information on a companion’s orbital period, eccentricity and even mass. However, since RVs only tell us about the component of
Figure 1: Doppler shift observable due to the motion of orbiting bodies. 18
the star’s motion aligned with our line of sight, orbit fitting using RVs alone suffers from a degeneracy between planet mass (M) and orbital inclination (i), the angle by which the companion’s orbit is tilted relative to our line of sight. Thus, it is impossible to determine planet masses using radial velocities alone. What we can compute is the product of the planet mass and its orbital inclination: M(sin(i)). Much like the transit method, the radial velocity method works best for massive, short period planets as these planets generate a higher amplitude radial velocity signal. Additionally, short periods make it possible to precisely fit the planet’s orbit and then subsequently confirm model predictions using future measurements. However, unlike transits, radial velocities maintain their usefulness even when applied to searches for planets located further out from their home star, since radial velocity measurements taken over a long baseline make it possible to study longer term trends. Still, extracting information about the nature of planets with orbital periods of a few decades (like Neptune and Uranus, the outer gas giants of the solar system) remains difficult. It is in this regime that a technique that may initially seem the most obvious of them all can help: directly imaging the exoplanet.
Image 1 When considering techniques that could be used to discover exoplanets, a natural thought to come to mind is the idea of simply imaging the system in question to look for planets directly. In practice, however, this sort of direct imaging remains highly difficult. Stars are usually much brighter than their planets, a property which results in them simply drowning out the light we receive from any planets that may lie in orbit. However, the last few years have seen great progress in direct imaging endeavors, an exciting result of which is the image of HR 8799 (Image 1). In the image, the black circle in the centre is the result of a technique known as coronagraphy, which blocks the light from the central star, allowing the light from the four planets in orbit to be more amenable to detection. The planet visible closest in is thought to have an orbit of around 45 years, while the planet furthest out orbits around once every 460 years. These are
the sort of long period planets that transits and radial velocities struggle with. Direct imaging, on the other hand, thrives for exactly these kinds of planets! When we image stars using telescopes, the nature of how their light spreads out on the detector is determined by its point spread function (PSF). The brightness of the PSF drops off with distance from the centre of the light distribution. Thus, in trying to image planets widely separated from their host stars, we have to contend with less of the star’s contaminating light. This makes long period planets most amenable to direct imaging. The ability of direct imaging to look for long period planets complements the effectiveness of transits and radial velocities at characterising shorter period planets! Furthermore, direct imaging can be used synergistically with radial velocities to determine the actual masses of planets rather than just M(sin(i))! The additional information provided by jointly using radial velocities and direct imaging can help us pin down the orbits and physical properties of long period planets to a much greater precision. While direct imaging is still in its relative infancy as a technique, the rapidly advancing nature of the field as well as the planned capabilities of next generation missions, such as the Nancy Roman Space Telescope, paint a bright picture for the future of exoplanet science. By making exoplanets at wide separations amenable for study and characterisation, direct imaging when used together with other techniques such as radial velocities, will allow us to gain a better picture of the population of planets that exist in our galaxy. Citations  [@NobelPrize]. (2019, October 8). The method used by research groups to find a planet is called the radial velocity method; it measures the movement of the host star as it is affected by the gravity of its planet. Retrieved from Twitter at https://twitter.com/NobelPrize/status/1181550417588768768/photo/1.  Kaufman, M. (2017, January 26). A four-planet system in orbit, directly imaged and remarkable. NASA Exoplanet Exploration. https://exoplanets.nasa.gov/news/1404/afour-planet-system-in-orbit-directly-imaged-and-remarkable/.
Astrophotography on the Cheap: A Guide Cameron Woo If you’re like me, you started in normal DSLR photography, and were drawn into astrophotography by seeing beautiful landscape shots of the Milky Way and wanted to do it on your own. Well you probably can’t do those because light pollution is everywhere (especially on the east coast of the US). But that doesn’t mean you can’t get beautiful astrophotography images. So here’s a guide for astrophotography with just a camera, lens, and tripod. Equipment People like to say that it’s not the tool, but how you use it. But that’s only true to an extent. For astrophotography, you need to collect a lot of light on your sensor (because these objects are dim). So you will need a camera that allows you to shoot long exposures with manual control. If you’re going from normal photography to astrophotography, that likely isn’t an issue. It means using a DSLR or mirrorless camera. (Or a dedicated astronomy camera, but if you’re already using that then you probably don’t need this guide). You will also need a tripod. This is pretty non-negotiable because you need a stable surface to mount your camera onto. Any shakes (even the slightest vibrations from pressing the shutter button) WILL make your image shaky and give you streaking stars. You can use whatever lens you want. For beginners, typically wider and faster is better. Faster, meaning a lower f-stop or wider aperture. This allows the camera to collect more light in less time. A wider lens also allows you to take longer exposures with less noticeable star trailing. There are online calculators, but I use the 500 rule to estimate an exposure length, then adjust based on my images. The 500 rule states that the maximum exposure time one should use is equal to 500/focal length. For “cheap” this equipment may run you around $300 for a camera, $100 for a tripod, and $300 for a lens, if you buy all new. So the used/refurbished market is a good place. However, many people even attempting this likely already have a camera and lens and tripod. If you’re fresh in, you should probably definitely get a star track20
er, but that’s a different tutorial. Imaging The key to getting good images is to take many photos of the same object and stack them in a program like Deep Sky Stacker (in Windows) or SiriL (in MacOS). There are four types of images (aka frames) that you should take: lights, darks, flats, and bias/offset frames. Note that all these images should be taken in a RAW file format with noise reduction turned off. RAW allows us to get every piece of data that the camera collects, unmodified. Noise reduction is achieved through collecting lots of data and calibration frames, so the camera’s internal noise reduction is unnecessary and possibly harmful to your final image. Where to Shoot/Bortle Scale Although this guide is meant for people living in heavily light polluted skies, that doesn’t mean you can’t use similar methods if you are lucky enough to drive to darker skies. The most common way to determine how dark your sky is is by using the Bortle scale. The scale ranges from 1-9, with 9 being the most light polluted, city sky at 1 being a truly dark sky, far far away from light pollution. There are many light pollution maps that can give you an idea for your Bortle scale, such as darksitefinder.com. These can also help you find any close areas that may be slightly better. Apps like Clear Outside can give you an actual number, but it can sometimes be inaccurate. It tells me my town is a Bortle 6, but the town right over is a Bortle 8 - and a mile difference isn’t going to drop your class by 2 levels, so be wary. The best way to assess your conditions is to go outside and look up! In the end, it won’t matter if the app says you live in Bortle 3 skies if your town just installed fancy, new, bright, blue LED street lamps in your cul de sac. Lights These are actual images of the object. For these, you
use as wide an aperture as you can, a slow shutter speed, and a high ISO. There are, of course, downsides to these decisions. A wide aperture can lead to distorted, T shaped stars in the corners of the image. These can be avoided by stopping down the lens, though you sacrifice how much light you collect. It’s up to you to play around and find the balance. If you need light, you can always crop the corners away. A slow shutter speed may introduce star trails, which we absolutely don’t want. So you must find a balance, using the 500 rule. High ISO creates a very noisy image.
cap on. These must be shot at the same ISO, shutter speed, as your lights and when the sensor is at the same temperature. This means they should be taken in between lights. You only need about 20 dark frames, though it never hurts to take more. They must be taken at every imaging session. These dark frames create a base noise pattern that will be removed from your stacked image. Flats
tern present inherently in the sensor. These frames should be taken at the fastest shutter speed possible, and at the same ISO as your lights. Aperture doesn’t matter. Take 10-20 bias frames. And that’s all the frames you need. Image stacking and processing is a whole other tutorial, so I’m not going to mention that here.
Flat frames are flat white pictures that help remove lens distortion like vignetting. They must be taken with the same lens and aperture settings as your
Living in light pollution is a real bummer, and may discourage you from shooting (especially when you take a photo and see a bright grey sky show up in your photo), but we can still make the most of the night sky. Apps like Clear Outside and websites like Clear Dark Sky will let you see detailed cloud cover and seeing/transparency conditions, which can drastically change the quality of your shots. Shooting high at the zenith (straight up) will let you shoot through the least amount of light pollution and atmosphere, so pick targets high in the sky. Also, choose large, bright targetsthe Orion Nebula is a great example. It’s bright and easy to find in the sword of Orion. And remember that a full moon will produce a TON of light pollution, so schedule your shooting around the new moon. Also, use planetarium software like Stellarium to plan your shots and explore new targets.
When and What to Image
Good luck and clear skies! An example of a Stellarium view. Preferably, we’d use a lower ISO, but since we aren’t using a star tracker we need a high ISO. Once again, find a balance between light and noise, but know that much noise will be removed in processing. The more lights you have, the more images you have to play around with and stack. So take as many lights as possible. In Bortle 8 skies, I like to take at least 70.
lights. One popular method is the “white T-shirt method”, where you take a white T-shirt, stretch it over the lens, and point it at the sky at dusk or dawn. We want to make the frame as evenly lit as possible, so the sky is a nice, large, diffused light source. Take about 10-20 flats. These frames are most easily taken in aperture priority mode. This way you know you’re collecting enough light. Bias
Darks These are images, but with the lens
These are similar to dark frames and are meant to remove the base noise pat21
Tulip Nebula (Sh2-101) © Ryan Caputo
The Horsehead Nebula (Barnard 33) © Wilson Zheng
The Orion Nebula (Messier 42) © Cameron Woo 22
Milky Way Galaxy over Etscorn Campus Observatory at New Mexico Tech © Owen Mitchell
The Moon © Nathan Sunbury
The Pleiades (Messier 45) © Cameron Woo
The Sunflower Galaxy (Messier 63) © Wilson Zheng
The Ring Nebula (Messier 57) © Nathan Sunbury
The Orion Nebula (Messier 42) and Running Man Nebula (Sh2-279) © Jonah Rolfness 24
Milky Way Galaxy, as seen from Kaanapali in Maui © Cameron Woo
The Pinwheel Galaxy (Messier 101) © Jonah Rolfness
Sadr Region © Jonah Rolfness 25
The Summer Triangle Asterism (Deneb-Cygnus, Vega-Lyra, Altair-Aquila) © Cameron Woo
The Hercules Cluster (Abell 2151) © Ryan Caputo
Star Trails Over Pierson College © Anavi Uppal
Messier 3 (near Bootes) © Wilson Zheng
The Heart Nebula (IC 1805) - Fish Head Nebula (IC 1795) and Melotte 15 Mosaic © Jonah Rolfness 27
Removing Noise and Improving the Quality of Astronomical Observations with Fourier Transforms Ezgi Zeren Dated: 15 September 2019 Abstract In this research, the quality of astronomical images was improved by eliminating the imperfections possibly caused by sky conditions or disturbances to a telescope. Using Fourier series and transforms, the images were processed in MATLAB to remove clouds, blur, smear, and vignetting. After Fourier-transforming the original images, a filter was multiplied with the Fourier transform of the images. Then, the inverse Fourier transform process was performed to obtain the filtered images. A high-pass sharp cut-off filter was used to emphasize the edges of astronomical images and to get rid of blur. In order to remove clouds, smear, and vignetting, a high-pass Gaussian filter was applied to the images. The resultant filtered images suggested an improvement in the image quality and displayed more distinguishable celestial objects. PACS numbers: 02.30.Nw, 06.30.â&#x2C6;&#x2019;k, 07.90.+c, 95.85.â&#x2C6;&#x2019;e Keywords: Astronomy, Fourier transforms, high-pass Gaussian filter, high-pass sharp cut-off filter, MATLAB I. Introduction
High-quality astronomical images are essential to the discovery of our universe. Astronomers try to make their images as clear and accurate as possible by using lossless file formats and processing the images. For more than four decades, astronomers have been using the Flexible Image Transport System (FITS) to interchange as much data as possible with lossless compression. FITS was so successful that even scientists started using it in digitizing manuscripts and medical imaging . After taking images with a professional telescope, astronomers use image processing to improve the quality of their images and the accuracy of their measurements. Even though image reduction is a well-developed technique to increase image quality, there are obstacles that sometimes prevent astronomers from obtaining the images they need, such as sky conditions and other disturbances from the environment. It may not be possible to change the sky conditions at a certain night or prevent the vibrations coming from the floor, but images taken at these circumstances can be improved to an extent that they are available for use in procedures that need precise
measurements such as the orbit determination of celestial objects. The data used in this research includes images taken with a 20-inch CCD reflecting telescope in the Sommers-Bausch Observatory at the University of Colorado Boulder as well as other images from astronomers around the world. The images included light cloud coverage, blur, smear, and vignetting. The impurities in the images were eliminated by applying a high-pass Gaussian filter or a high-pass sharp cut-off filter created in MATLAB, using Fourier series and transforms. Since the impurities were light, they produced a low-frequency noise, which made it possible to eliminate them without damaging the necessary data. II. Data Acquisition A. Observations and Image Reduction The image of Saturn, FIG. 9, was obtained using the PlaneWave Telescopes in the Sommers-Bausch Observatory at the University of Colorado Boulder . The observatory was located at a longitude of 105.2630 W, a
latitude of 40.0037 N, and an altitude of 1653 meters. The two 20-inch (508 mm) telescopes, Artemis and Apollo, were CDK20 Corrected Dall-Kirkham Astrograph carbon-fiber truss telescopes and had a focal length of 3454 mm and a focal ratio of f/6.8. Without any off-axis coma, astigmatism, and field curvature, the CDK20 telescopes had a 52 mm field of view. They also used CCD software for imaging. A Bahtinov Mask , a device that is used for achieving a high-level accuracy when focusing, was attached to focus the telescope during observations, and the telescope was slewed to a star of magnitude 2 to 4. After focusing as precisely as possible, the Bahtinov Mask was removed, and the telescope was slewed to Saturn. Then, a test image was taken to ensure that there is no issue with focusing and that the telescope is slewed to the correct right ascension and declination. In order to do image reduction, three types of images were taken with the telescopes: light frames, flat frames, and dark frames .
Dark frames were the images that contain no light, and they were used to eliminate the effect of having different signal readings from the camera sensors. FIG. 3 shows an example of a dark frame. The exposure time for the images was 70 seconds in all of the observations, since it was enough to have a high-quality image.
Figure 3: An example dark frame. The image reduction was done with AstroImageJ, an open source image processing software. The dark frames were subtracted from the light frames and the subtracted image was divided by a normalized version of the flat frames. This process made the reduced images of Saturn almost free of all imperfections that are caused by the telescopes and the imaging CCD arrays. Thus, the only problem was the blur caused by the resolution of the telescopes. Figure 1: An example light frame. Light frames were the frames that contained the image of the celestial bodies in the sky. If it is not possible to perform image reduction, astronomers only use the light frames as their data, because the only necessary data about the celestial objects is stored in the light frames. Other frames are only used to further improve the quality of the images by eliminating possible imperfections caused by the equipment. FIG. 1 shows an example of a light frame, an image of stars in the sky. Flat frames were the frames that only contain an image of a white wall, and that were used to eliminate the optical imperfections such as the effect of having dust on the sensors. FIG. 2 shows an example of a flat frame. Figure 2: An example flat frame.
B. Data from Other Astronomers The image of the Eagle Nebula, the Pillars of Creation, FIG.4, was taken by Professor Peter Coppinger , Associate Professor of Biology at Rose-Hulman Institute of Technology, who is an amatuer astrophotograher. The image with the light cloud coverage, FIG. 14, was taken by Rob Pettengil  with the Sony RX100V camera at ISO 400, with a focal ratio of f/2 and a focal length of 24 mm. The exposure time was 2 minutes. The image with smearing, FIG. 24, was taken by Mike Dodd, Louise Kurylo, Michelle Dodd and Miranda Dodd at the Hidden Creek Observatory  with a Astro-Tech AT130EDT refractor, which has an aperture of 130 mm (5.12â&#x20AC;?), a focal length of 910 mm, and a focal ratio of f/7. The image with vignetting, FIG.19, was taken by Jerry Lodriguss  with a 12.5 inch (317.5 mm) Dobsonian Telescope that has a focal ratio of f/6 and a focal length of 75 inches (1905 mm). III. Methods A. The Fourier Transform  Every function can be expressed as a waveform that is composed of sines and cosines. Since humans became very successful at horology , the study of time measure-
ment, scientists have been trying to use frequencies in their experiments for better accuracy. The Fourier Transform is a useful tool that describes a given waveform as the sum of sinusoids of different frequencies, amplitudes, and phases. If a function Φ(p) has a Fourier Transform F(x), then Φ(p) is also the Fourier Transform of F(x). This “Fourier Pair” , Φ(p) and F(x) is defined as
C. The Two-Dimensional DFT In this research, images will be Fourier transformed. Thus, a two-dimensional Discrete Fourier Transform is done with the FFT algorithm. Instead of the formulae for the one-dimensional case, Eq. (3) and Eq. (4), the two-dimensional DFT has the formulae
B. The Fast Fourier Transform Algorithm (FFT) and the Discrete Fourier Transform (DFT)  The Discrete Fourier Transform (DFT) is the mutual transformation of a pair of sets, [an] and [Am], such that each contain N elements. The formulae for DFT are
where m and n are the rows and columns of g[m,n], and j and k are the rows and columns of G[j,k] columns. The Fourier Transform Matrix G[j,k] is obtained after Fourier transforming the data matrix g[m,n]. Then, a filter can be applied to G[j,k], and the filtered g[m,n] matrix can be found by the inverse DFT synthesis process, where the input matrix is the filtered G[j,k] as in Eq. (6). D. Using MATLAB for Fourier Transforming FITS, png, and jpg Images
If , the formulae for DFT can be set out as a matrix operation as follows.
As shown in section III. A., the Fourier transformation process involves integral calculus which can cause a problem when the integrand is an experimental data set instead of an analytical function. The Fast Fourier Transform algorithm (FFT) was discovered to deal with complex integrands like these. This algorithm reads the data into an array and returns the transformed data points. The matrix operation above requires N2 multiplications. However, the fast Fourier transform (FFT) reduces the number of multiplications necessary from N2 to about N/2log2(N). 30
As mentioned in section I, FITS images were specifically tailored for Astronomy, and they have a special but expanding usage amongst the scientific community. Therefore, the image processing functions in the Image Processing Toolbox of MATLAB, are not capable of processing FITS files. However, MATLAB has a special function to read FITS files, called fitsread() . After reading the FITS files, with fitsread(), the imagesc() function was used to show the image because imagesc() displays the image with a scaled data. Then, the colormap was set to be gray rather than the default color, blue, for aesthetic purposes.
For png and jpg images, imread() function was used to read the files. Then, the images were converted to gray scale with rgb2gray() function. For displaying the gray image, imshow() function was used.
MATLAB has its own two-dimensional Fast Fourier Transform function called fft2(). This function, along with the fftshift() function was used to place the low frequencies in the center of the image. Since it was difficult to visualize complex numbers, the magnitudes were displayed as a matrix. Also, having the sum of the values in the center might have caused other magnitudes to be dwarfed when compared to the center. This was prevented by taking the logarithm of the values. The logarithm was taken in the form of log(1 + matrix element value) to avoid the possibility of having negative results when the matrix element value is less than 1. The subroutine fftshow.m, written by Alasdair McAndrew of Victoria University of Technology, was used to display the two-dimensional DFT. The subroutine can be found in the appendix. The command impixelinfo displays pixel information such as the pixel value, which is helpful to check that nothing goes wrong with the Fourier transform.
E. Edge Detection of the Images of Nebulae Using MATLAB The identification of a nebula against the sky can be challenging when the image taken has a significant amount of noise, which is usually caused by sky conditions such as air pollution. There is noise in the image of the Eagle Nebula, the Pillars of Creation, FIG. 4, which makes it difficult to see the nebulaâ&#x20AC;&#x2122;s original shape. The image also contains a slight amount of vignetting, which causes the image to lose details in the corners and edges of the picture.
Figure 5: Fourier transform of the Eagle Nebula. The high-pass sharp cut-off filter has the pixel value of 0 in the center and the pixel value of 1 everywhere else, in order to block the low frequencies and only pass the high frequencies when multiplied with the Fourier transform. The high-pass sharp cut-off filter was created in the shape of a circle in order to block the desired low frequencies in two dimensions. Since the filter was multiplied with the Fourier transform, the size of the meshgrid was set to be same with the original image, 541x542. The equation of a circle was used to create a circle, and the radius of the circle was set to be 0.2 pixels because after trying several radii, 0.2 was determined to be functioning well for edge detection. The code for creating the filter is below.
Since the radius of the filter was 0.2 pixels, the filter appears to be the shape of a square instead of a circle, and the filter is hard to see in the filtered Fourier transform, FIG. 7; however, 0.2 pixels was enough for edge detection.
Figure 4: Original image of the Eagle Nebula. After performing the Fourier transform with the code in section III. D., FIG. 5 was obtained. The image of the Fourier transform shows the low frequencies in the center and high frequencies when moving away from the center. Since the noise has a low frequency, a high pass sharp cut-off filter was used for edge-detection. Figure 6: The high-pass sharp cut-off filter for the nebula.
The filter and the Fourier transform were multiplied in order to eliminate the low frequencies and emphasize the high frequencies. The filtered Fourier transform, FIG. 7, was set to grayscale since it was making it easier to compare the filtered Fourier transform from the original Fourier transform.
After the application of the filter, the edges of the Eagle Nebula became clearer to the observer, meaning that the high-pass sharp cut-off filter was successful at performing edge detection on the image of the nebula. F. Edge Detection of the Rings of Saturn Using MATLAB Even after processing the astronomical images with image reduction and using the highest quality way to store the images with FITS files, astronomical images might not be clear enough. There is always a limit of resolution that a certain telescope can reach. The image of Saturn, FIG. 9, was reduced with the method described in section II. A. However, the rings of Saturn were not clear enough because of the restriction of the limits of resolution of the telescope .
Figure 7: The filtered Fourier transform of the nebula. Finally, the inverse Fourier transform was applied to the filtered Fourier transform, FIG. 7, using the ifft2() function of MATLAB. The ifft2() function is for two dimensional inversion Fourier transformation, which is necessary to finalize the filtering of the images used in this research.
Figure 9: The original image of Saturn. Since the image of Saturn was stored in a FITS file, the code in section III.D. for FITS images was used, and the Fourier transform was performed in the same way described in section III.F.
Figure 8 displays the image of the Eagle Nebula after the high-pass edge detection filter was applied.
Figure 8: The filtered image of the nebula. 32
Figure 10: The Fourier transform of Saturn.
Since the determination of the rings of Saturn requires edge detection, a high-pass sharp cut-off filter was used for the image of Saturn as well. However, the original image size and the radius of the filter were much bigger.
Figure 13: The filtered image of Saturn. E. Edge Detection of the Images of Nebulae Using MATLAB
Figure 11: The high-pass sharp cut-off filter for Saturn. The high-pass filter was multiplied with the Fourier transform to produce the filtered Fourier transform in the same way described in section III.F. Since the radius of the filter was 20 pixels, its circular shape was more distinct this time, when compared with the high-pass filter used for the Eagle Nebula.
Since the light cloud coverage and the blur caused by the sky conditions in astronomical images have a from similar to that of a Gaussian function, they can be eliminated with a Gaussian high-pass filter . FIG. 14 displays a light cloud coverage which makes it hard to determine the stars in the sky.
Figure 14: Original image of the light cloud coverage. The image was read and Fourier-transformed with the code displayed in section III.D.
Figure 12: The filtered Fourier transform of Saturn. The Fourier-transformed image of the Eagle Nebula after the high-pass edge detection filter is applied is displayed in FIG. 13. After the application of the sharp cut-off filter to the image of Saturn, the rings of Saturn became more recognizable. The rings got darker while the background got brighter, making the edges of the rings very clear.
Figure 15: The Fourier transform of the cloud coverage. 33
The Gaussian function, Eq. (7), creates a Gaussian filter with standard deviation Ď&#x192;. The standard deviation value determines the strength of the filter, as the standard deviation value gets higher, the filter becomes more distributed, and when the value gets lower, the filter becomes more dense.
In order to create the high-pass Gaussian filter, the discretization  of the Gaussian function was subtracted from 1, Eq. (8), making the pixel values in the center equal to 0. The MATLAB code for Eq. (8) is provided below.
Figure 17: The filtered Fourier transform of the cloud coverage. Figure 18 is the Fourier-transformed image of the light cloud coverage after the high-pass Gaussian filter is applied. The inverse Fourier transformation process was performed as described in section III. E.
Figure 18: The filtered image with cloud coverage.
Figure 16: The high-pass Gaussian filter for cloud coverage. Figure 16 shows the zoomed in version of the high-pass Gaussian filter. The smooth edges of the Gaussian filter seen in FIG.16. are very useful for eliminating noise. In addition, using a Gaussian filter prevents the ringing effect, a wavy appearance near the edges of images due to the loss of high frequency information. With images with cloud coverage, not having a ringing effect is crucial, since the ringing effect might also ruin the images.
As can be seen from the filtered image, FIG. 18, the clouds are not visible to the observer anymore. This suggests that the cloud removal with the high-pass Gaussian filter was successful. Moreover, after the removal of the clouds, the stars became more apparent, and their shape became more circular, since the noise around the stars was also eliminated. H. Getting Rid of Vignetting in Astronomical Images Using MATLAB Vignetting is caused by the design of the sensor and the lens of a camera; therefore, it is unavoidable in every image. The light coming towards the center of a camera sensor strikes at a right angle, but the angle of incidence decreases further from the center of the censor, which
causes vignetting. However, vignetting can be present in different amounts in different images which makes its effects nearly invisible in some images. The image of the sky, Figure 19, has a visible amount of vignetting in the corners and edges, and a slight noise in the center, which ruins the clarity of the image. Since it is light enough, the noise in the center can be eliminated, using a high-pass Gaussian filter. After eliminating the noise, the sky will be more visible and the uneven distribution of the light due to vignetting will be eliminated.
Figure 21: The high-pass Gaussian filter for vignetting. Since the standard deviation of the Gaussian filter was small, the filter appears to be like a black point in the center of the filtered Fourier transform of the image with vignetting, FIG. 22. Even though it seems to be too small to have a significant effect in the image, the filter cancels out enough of the noise in the image. Figure 19: The sky image with vignetting. The image was Fourier transformed as described in section III. D. As can be seen from the Fourier transform of the image of the sky, the low frequencies in the center of the image have a small radius. Thus, a Gaussian filter with 2.5 standard deviation was used to eliminate the noise.
Figure 22: The filtered Fourier transform of the image with vignetting.
Figure 20: The Fourier transform of the image with vignetting.
Figure 23 displays the Fourier-transformed image of the Sky with vignetting after the high-pass Gaussian filter was applied. The inverse Fourier transformation process was performed as described in section III. E. The resultant filtered image no longer has uneven brightness distribution, and the edges of the image do not have a darker color than the center of the image. Since the noise in the center of the image was eliminated and the effects of vignetting was canceled out, the application of the high-pass Gaussian filter was successful.
The image with smearing was Fourier transformed as described in section III.D. The Fourier transform of the image with smearing has a small radius of distinguishable low frequencies in the center, but since the smearing was harsher than a slight noise, a Gaussian filter with a relatively higher standard deviation was used.
Figure 23: The filtered image with vignetting. I. Eliminating Smearing in Astronomical Images Using MATLAB Smearing in astronomical images is usually caused by factors independent of the telescope. If a telescope is disturbed during its exposure time, smearing will probably occur. This can be prevented by removing any disturbances near the telescope. However, the vibrations from the floor can be an unavoidable factor that causes smearing. In situations like this, the images can be recovered by applying a high-pass Gaussian filter to treat the smear as a noise around the actual celestial objects. Figure 24 shows the image of stars with smearing. Instead of being circular, the stars seem to look like ellipses. This image can be recovered with a high-pass Gaussian filter because the smeared parts of the stars have a lower frequency than the actual parts of the stars. If the smear was as light as the stars, then recovering with a filter might not be possible.
Figure 25: The Fourier transform of the sky image with smearing. The high-pass Gaussian filter used for the smearing, FIG. 26, has a standard deviation of 8, which cancels out a more distinct amount of noise from the original image.
Figure 26: The high-pass Gaussian filter for smearing.
Figure 24: The sky image with smearing.
As can be seen from the filtered Fourier transform of the smear, Figure 27, the high-pass Gaussian filter used for smearing is very visible in the center.
Figure 27: The filtered Fourier transform of the smeared image. FIG. 28 displays the image with smearing after the high-pass edge detection filter is applied, and the image is Fourier-transformed. The inverse Fourier transformation process was performed as described in section III. E. Since the background noise from the sky lessened clearly, the high-pass Gaussian filter was successful at removing the noise around the images of stars with smearing. In addition, the shape of the stars became more circular, when compared to their initial smeary shape. Thus, the high-pass Gaussian filter improved the quality of the image.
Figure 28: The filtered sky image with smearing. IV. Conclusion In this research, astronomical images with blur, smearing, light cloud coverage, and vignetting were processed with MATLAB. Image processing was done with Fourier series and transforms by creating high-pass Gaussian and sharp cut-off filters. After Fourier-transforming the images and shifting the zero-frequency components of the discrete Fourier transforms to the center, the noise in the center was multiplied with the filters and eliminated.
In order to emphasize the edges of the Eagle Nebula and to make the rings of the Saturn more clear, high-pass sharp cut-off filters with different radii were created. The radii of the filters were determined by trial and error. High-pass Gaussian filters were used to cancel out the effects of vignetting, smearing, and blur. Since the noise created by these factors was similar to a Gaussian noise, the high-pass Gaussian filter was successful at eliminating a portion of the noise, making the images more clear. A high-pass Gaussian filter was also used to cancel out the light cloud coverage in an image of the sky. The Gaussian filter was suitable for eliminating the clouds because the clouds had a low frequency and blurry edges. The highpass Gaussian filters used in different images had different standard deviations. The best standard deviation value for an image was again determined by trial and error. Since the Fourier transform of the image with light cloud coverage had more low frequencies in the center than others, the highest value of standard deviation was used to eliminate the light cloud coverage. After multiplying the Fourier transforms of the images with the filters created in MATLAB, the inverse Fourier transform process was performed in order to obtain the filtered image. After the application of the high-pass sharp cut-off filter, the original image of the Eagle Nebula, the Pillars of Creation, Figure 4, became more evident. As can be seen from the filtered image of the Eagle Nebula, Figure 8, the edges of the nebula are more emphasized, and the stars are more distinguishable from the background. The rings of Saturn in the original image, Figure 9, were blurry before being filtered. However, after the high-pass sharp cut-off filter was applied, the rings appear in black around Saturn. The rings are more recognizable in the filtered image of Saturn, Figure 13. The image with light cloud coverage, Figure 14, was covered with noise, and the stars were difficult to see. After using a high-pass Gaussian filter, the clouds, which were light enough to be treated like a Gaussian noise, were eliminated. As can be seen from the filtered image of the sky, Figure 18, the stars are more distinguishable from the background after the clouds are removed. The sky image with vignetting, Figure 19, has uneven brightness distribution due to vignetting. The effect of vignetting was eliminated with a high-pass Gaussian filter. The resultant filtered image, Figure 23, no longer has uneven brightness distribution, and the stars are more recognizable. With the application of the high-pass Gaussian filter, the original sky image with smearing, Figure 24, looks less noisy. The stars in the filtered sky image with smearing, Figure 24, are more circular, and their edges are more distinct. This research proposed a method for the removal of clouds, blur, smearing, and vignetting from astronomical images with Fourier series and transforms, using MATLAB. Different amounts of noise was eliminated from dif37
ferent images, since the success of the filtering process also depended on how much noise was present in the original image. When compared with the original images, the resultant filtered astronomical images suggest that the filters worked successfully to improve the quality of the astronomical data. Acknowledgements This research was guided by the Pioneer Academics Program. Special thanks to Professor Arthur Western for always being a source of help and advice. Appendix: The fftshow() Subroutine
References  Thomas, “Learning from FITS: Limitations in use in modern astronomical research,” Astronomy and Computing 12, 133-145 (2015)  Sommers-Bausch Observatory (2018)  R. Berry and J. Burnell, The Handbook of Astronomical Image Processing (Willmann-Bell, Richmond, VA, 2011)  W. Romanishin, An Introduction to Astronomical Photometry Using CCDs, (University of Oklahoma, 2006), pp. 79-80  P. Coppinger, https://www.rose-hulman.edu/academics/faculty/coppinger-jpeter-coppinge.html  R.Pettengil, http://astronomy.robpettengill.org/ blog180114.html  M. Dodd, http://astronomy.mdodd.com/flexure.html  J. Lodriguss, http://www.astropix.com/html/jdigit/vi38
gnet.html  Ronald N. Bracewell, The Fourier Transform and its Applications, International Editions, 3rd Ed., McGraw-Hill  M.of Time, Mathematics, https://www.encyclopedia.com/science-and-technology/mathematics/mathematics/measurement-time  J.F. James, A Student’s Guide to Fourier Transforms: with Applications in Physics and Engineering (Cambridge University Press, Cambridge, 1995)  A. Hayes and J. Bell, http://hosting.astro.cornell. edu/academics/courses/astro3310/Matlabimages.html  Lea, S. M. & Kellar, L. A., An algorithm to smooth and find objects in astronomical images, (Astronomical Journal, 1989), pp. 1238-1246  Hummel, R. A., Kimia, B., and Zucker, S. W., “Deblurring Gaussian blur. Computer Vision, Graphics, and Image Processing,” 66–80 (1987)  A. Torralba, http://6.869.csail.mit.edu/fa16/lecture/ lecture3linearfilters.pdf
Computational Astrophysics Advancements of 2020 Priti Rangnekar
Depiction of lower error for the D3M model compared to the earlier 2LPT model . Traditionally, the words “astronomy” and “astrophysics” may conjure images of ancient star charts, telescopes staring into the night sky, or chalkboards filled with Einstein’s equations detailing special and general relativity. However, with the rise of ground and space-based sky survey projects and Citizen Science endeavors involving contributions from amateur astronomers worldwide, the field of astronomy is becoming increasingly data-driven and computationally enhanced. Survey projects, such as The Large Synoptic Survey Telescope, bring data issues such as high volume (nearly 200 petabytes of data), large varieties of data, and rapid speeds of data production and transmission, requiring efficient analysis through statistical computing.  As we collect more information about the physical world and develop powerful software and hardware, we gain the ability to methodically find patterns and make large scale predictions based on what we do know, allowing us to embrace the frontier of what has always been unknown. In June 2019, researchers from institutions including Carnegie Mellon University and the Flatiron Institute announced the development of the first artificial intelligence simulation of the universe - the Deep Density Displacement Model. With the ability to complete a simulation in less than 30 milliseconds, the model proved to be both efficient and accurate, with relative errors of less than 10% when compared with both accurate but slow models and fast but less accurate models. Moreover, it could provide accurate values for certain physical values, such as dark
matter amount, even when tested with parameters, such as gravitational conditions, it was not originally trained on. This is just one example of how the power of computing techniques can allow us to better understand the universe and its past.  In 2020, research groups from around the world have further capitalized on artificial intelligence and supercomputing to analyze specific aspects of the universe, including exoplanets, galaxies, hypernovae, and neutron star mergers. Gaussian Process Classifiers for Exoplanet Validation University of Warwick scientists Armstrong, Gamper, and Damoulas recently capitalized on the power of machine learning to develop a novel algorithm for confirming the existence of exoplanets, which are planets that orbit stars outside the Solar System.  Traditionally, exoplanet surveys use large amounts of telescope data and attempt to find evidence of an exoplanet transit, or any sign of the planet passing between the telescope and the star it is orbiting. This typically comes in the form of a dip in the observed brightness of the target star, which makes intuitive sense given that the planet would be obstructing some light. Nevertheless, this analysis can be prone to false positive errors, given that an observed dip does not necessarily indicate the presence of an exoplanet; it could also be caused by camera errors, background object interference, or binary star systems. In the case of
a binary star system, eclipsing binaries may result, in which a starâ&#x20AC;&#x2122;s brightness would vary periodically as one passes in front of the other, causing the observed dip. Such a phenomenon would require extended analysis of the target starâ&#x20AC;&#x2122;s flux lightcurve, which shows changes in brightness. In the case of background object interference, a background eclipsing binary or planet may blend with the target star, requiring researchers to observe any offset between the target star and the transit signal.  As a result, researchers use a planetary validation process in order to provide the statistical probability that a transit arose from a false positive, in which a planet was not present.  A common algorithm used for validating some of the approximately 4,000 known exoplanets has been the vespa algorithm and open source code library. The procedure, detailed in a paper by Morton in 2012, accounts for factors such as features of the signal, target star, follow-up observations, and assumptions regarding field stars.  However, as Armstrong, Gamper, and Damoulas explain in their abstract published in August 2020, a catalogue of known exoplanets should not be dependent on one method.  Previous machine learning strategies have often generated rankings for potential candidates based on their relative likelihoods of truly being planets; however, these approaches have not provided exact probabilities for any given candidate. For example, in 2017, Shallue and Vanderburg developed a model that generated rankings for potential candidates based on their relative likelihoods of truly being planets. 98.8% of the time, plausible planet signals in the test set were ranked higher than false positive signals.  However, a probabilistic framework is a key component of the planetary validation process. Thus, by employing a Gaussian Process Classifier along with other models, the University of Warwick researchers could find the exact statistical probability that a specific exoplanet candidate is a false positive, not merely a relative ranking. In general, a Gaussian Process generates a probabilistic prediction, which allows researchers to incorporate prior knowledge, potentially find confidence intervals and uncertainty values, and make decisions about refitting.  If the probability of a candidate being a false positive is less than 1%, it would be considered a validated planet by their approach. Trained using two samples of confirmed planets and positive samples from Kepler, the model was tested on unconfirmed Kepler candidates and confirmed 50 new planets with a wide range of sizes and orbital periods. 
A depiction of an exoplanet transit lightcurve; the Gaussian Process Classifier prioritizes the ingress and egress regions, indicated by the 2 dotted lines, when classifying exoplanets .
Although the computational complexity for training the model is higher than that of traditional methods, and certain discrepancies with vespa were found, this approach demonstrates a clear potential for efficient automated techniques to be applied for the classification of future exoplanet candidates, while becoming more accurate with each dataset due to machine learning. In fact, the researchers aim to apply this technique to data from the missions PLATO and TESS, which has already identified over 2,000 potential exoplanet candidates.  Machine Learning and Deep Learning for Galaxy Identification and Classification An example of data augmentation for galaxy images using rotation and flipping .
Another area of artificial intelligence growing in popularity is image classification and object detection, with common applications for autonomous vehicles and medical imaging. A powerful technique in this field is a convolutional neural network, a form of deep learning roughly based on the functionalities and structure of the human brain. Each layer of the network serves
a unique purpose, such as convolution layers for generating feature maps from the image, pooling layers for extracting key features such as edges, dense layers for combining features, and dropout layers that prevent overfitting to the training set.  This method was applied to galaxy classification by researchers at the National Astronomical Observatory of Japan (NAOJ). The Subaru Telescope, an 8.2-meter optical-infrared telescope at Maunakea, Hawaii, serves as a robust source of data and images of galaxies due to its wide coverage, high resolution, and high sensitivity.  In fact, earlier this year, astronomers used Subaru Telescope data to train an algorithm to learn theoretical galaxy colors and search for specific spectroscopic signatures, or light frequency combinations. The algorithm was used to identify galaxies in the early stage of formation from data containing over 40 million objects. Through this study, a relatively young galaxy HSC J1631+4426, breaking the previous record for lowest oxygen abundance, was discovered.  In addition, NAOJ researchers have been able to detect nearly 560,000 galaxies in the images and have had access to big data from the Subaru/Hyper Suprime-Cam (HSC) Survey, which contains deeper band images and has a higher spatial resolution than images from the Sloan Digital Sky Survey. Using a convolutional neural network (CNN) with 14 layers, they could classify galaxies as either non-spirals, Z-spirals, or S-spirals.  This application presents several important takeaways for computational astrophysics. The first is the augmentation of data in the training set. Since the number of non-spiral galaxies was significantly greater than the number of spiral galaxies, the researchers needed more training set images for Z-spiral and S-spiral galaxies. In order to achieve this result without actively acquiring new images from scratch, they flipped, rotated, and rescaled the existing images with Z-spiral and S-spiral galaxies, generating a training set with roughly similar numbers for all types of galaxies.
Second, it is also important to note that the accuracy levels of AI models may reduce when working with celestial bodies or phenomena that are rare, due to a reduction in the size of the training set. The galaxy classification CNN originally achieved an accuracy of 97.5%, identifying spirals in over 76,000 galaxies in a testing dataset. However, this value decreased to only 90% when the model was trained on a set with fewer than 100 images per galaxy type, demonstrating the potential for concerns if more rare galaxy types were to be used. A final important takeaway is regarding the impact of misclassification and differences between the training dataset and the testing dataset. When applying the model to the testing set of galaxy images to classify, the model found roughly equal numbers of S-spirals and Z-spirals. This contrasted with the training set, in which S-spiral galaxies were more common. Although this may appear concerning, as one would expect the distribution of galaxy types to remain consistent, the training set may have not been representative, likely due to human selection and visual inspection bias. In addition, the authors point out that the criterion of what constitutes a clear spiral is ambiguous, and that the training set images were classified by human eye. As a result, while the training set only included images that had unambiguous spirals; the validation set may have included more ambiguous cases, causing the model to incorrectly classify them. Several strategies can be used to combat such issues in scientific machine learning research. In terms of datasets, possible options include creating a new, larger training sample or employing numerical simulations to create mock images. On the other hand, a completely different machine learning approach unsupervised learning - could be used. Unsupervised learning would not require humans to visually classify the training dataset, as the learning model would identify patterns and create classes on its own.  In fact, researchers at the Computational Astrophysics Research Group at the University of Santa Cruz have tak-
en a very similar approach to the task of galaxy classification, focusing on galaxy morphologies, such as amorphous elliptical or spheroidal. Their deep learning framework, named Morpheus, takes in image data by astronomers and uniquely does pixel level classification for various features of the image, allowing it to discern unique objects within the same image rather than merely classifying the image as a whole (like the models used by the NAOJ researchers). A notable benefit of this approach is that Morpheus can discover galaxies by itself and would not require as much visual inspection or human involvement, which can be fairly high for traditional deep learning approaches - the NAOJ researchers worked with a dataset that required nearly 100,000 volunteers.  This is crucial, given that Morpehus could be used to analyze very large surveys, such as the Legacy Survey of Space and Time, which would capture over 800 panoramic images per night. 
Examples of a Hubble Space Telescope Image and its classification results using Morpheus .
Supercomputing for Analyzing Hypernovae and Neutron Star Mergers Given the data-intensive nature of this endeavor as well as the need for intensive pixel-level classification, it is natural to wonder how scientists are able to run such algorithms and programs in the first place. The answer often lies in supercomputing, or high performance computing (HPC). Often Supercomputers often involve interconnected nodes that can communicate, use a technique called parallel processing to solve multiple computational problems via multiple CPUs or GPUs, and can rapidly input and output data.  This makes them prime candidates for mathematical modeling of complex systems, data mining and analysis, and performing operations on matrices and vectors, which are ubiquitous when using computing to solve problems in physics and astronomy.  The robust nature of supercomputing was recently seen, as researchers from the Academia Sinicaâ&#x20AC;&#x2122;s Institute of Astronomy and Astrophysics used the supercomputer at the NAOJ to simulate a hypernova, which is potentially 100 times more energetic than a supernova, resulting from the collapse of a highly massive star. The program simulated timescales nearly an order of magnitude higher than earlier simulations, requiring significantly higher amounts of computational power while allowing researchers to analyze the exploding star 300 days after the start of the explosion.  However, this was indeed beneficial, as the longer timescale enabled assessment of the decay of nickel-56. This element is created in large amounts by pair-instability supernovae (in which no neutron star or black hole is left behind) and is responsible for the visible light that enables us to observe supernovae. Moreover, we cannot underestimate the importance of simulations, as astronomers cannot rely on observations given the rarity of hypernovae in the real world.  Supercomputers have also been used for simulating collisions between 2 neutron stars of significantly different masses, revealing that electromagnetic radiation can result in addition to grav42
itational waves.  Once again, we can see the usefulness of computational simulations when real observations do not suffice. In 2019, LIGO researchers detected a neutron star merger with 2 unequal masses but were unable to detect any signal of electromagnetic radiation. Now, with the simulated signature, astronomers may be capable of detecting paired signals that indicate unequal neutron star mergers. In order to conduct the simulations using the Bridges and Comet platforms, researchers used nearly 500 computing cores and 100 times as much memory as typical astrophysics simulations due to the number of physical quantities involved.  Despite the tremendous need for speed, flexibility, and memory, supercomputers prove an essential tool in modeling the intricacies of our multifaceted universe.
A 3-D visualization of a pair-instability supernova, in which nickel-56 decays in the orange area .
ATERUI II, the 1005-node Cray XC50 system for supercomputing at the Center for Computational Astrophysics at the NAOJ .
Conclusion Undoubtedly, scientific discovery is at the essence of humankind, as our curiosity drives us to better understand and adapt to the natural and physical world we live in. In order to access scientific discovery, we must have the necessary tools, especially as the questions we ask are becoming more complex and data is becoming more ubiquitous. Outer space continues to feature so many questions left to answer, yet with profound implications for humankind. The overarching, large-scale nature of the physical processes that govern celestial bodies begs for further research and analysis to learn more about unknown parts of the universe. Yet, we are now better equipped than ever to tackle these questions. We can find trends in the seemingly unpredictable and using logic, algorithms, and data through computer programs, creating a toolbox of methods that can revolutionize astronomy and astrophysics research. Ultimately, as we strive to construct a world view of how the universe functions, we will be able to make the most of large portions of data from a variety of research institutions while fostering collaboration and connected efforts by citizens, scientists, and governments worldwide. Citations  Zhang, Y., & Zhao, Y. (2015). Astronomy in the Big Data Era. Data Science Journal, 14(0), 11. doi:10.5334/dsj-2015-011  Sumner, T. (2019, June 26). The first AI universe sim is fast and accurate-and its creators donâ&#x20AC;&#x2122;t know how it works. Retrieved November 25, 2020, from https://phys.org/news/2019-06-ai-universe-sim-fast-accurateand.html  Armstrong, D. J., Gamper, J., & Damoulas, T. (2020). Exoplanet Validation with Machine Learning: 50 new validated Kepler planets. Monthly Notices of the Royal Astronomical Society. doi:10.1093/mnras/staa2498  S. T. Bryson, M. Abdul-Masih, N. Batalha, C. Burke, D. Caldwell, K. Colon, J. Coughlin, G. Esquerdo, M. Haas, C. Henze, D. Huber, D. Latham, T. Morton, G. Romine, J. Rowe, S. Thompson, A. Wolfgang, 2015, The Kepler Certified
False Positive Table, KSCI-19093-003  Staff, S. (2020, August 25). 50 new planets confirmed in machine learning first. Retrieved November 25, 2020, from https://phys.org/ news/2020-08-planets-machine.html  Morton, T. D. (2012). AN EFFICIENT AUTOMATED VALIDATION PROCEDURE FOR EXOPLANET TRANSIT CANDIDATES. The Astrophysical Journal, 761(1), 6. https://doi. org/10.1088/0004-637x/761/1/6  Shallue, C. J., & Vanderburg, A. (2018). Identifying Exoplanets with Deep Learning: A Five-planet Resonant Chain around Kepler-80 and an Eighth Planet around Kepler-90. The Astronomical Journal, 155(2), 94. https://doi.org/10.3847/1538-3881/ aa9e09  1.7. Gaussian Processes — scikitlearn 0.23.2 documentation. (2020). Scikit-Learn.Org. https://scikit-learn. org/stable/modules/gaussian_process.html  Yeung, J., & Center/NASA, D. (2020, August 26). Artificial intelligence identifies 50 new planets from old NASA data. Retrieved November 25, 2020, from https://news. lee.net/news/science/artificial-intelligence-identifies-50-new-planets-from-old-nasa-data/article_556fdd68-e7ad-11ea-85cb-87deec2aa462.html  Tadaki, K.-, Iye, M., Fukumoto, H., Hayashi, M., Rusu, C. E., Shimakawa, R., & Tosaki, T. (2020). Spin parity of spiral galaxies II: a catalogue of 80 k spiral galaxies using big data from the Subaru Hyper Suprime-Cam survey and deep learning. Monthly Notices of the Royal Astronomical Society, 496(4), 4276–4286. https:// doi.org/10.1093/mnras/staa1880  Overview of Subaru Telescope: About the Subaru Telescope: Subaru Telescope. (n.d.). Retrieved November 25, 2020, from https://subarutelescope.org/en/about/  Kojima, T., Ouchi, M., Rauch, M., Ono, Y., Nakajima, K., Isobe, Y., Fujimoto, S., Harikane, Y., Hashimoto, T., Hayashi, M., Komiyama, Y., Kusakabe, H., Kim, J. H., Lee, C.-H., Mu-
kae, S., Nagao, T., Onodera, M., Shibuya, T., Sugahara, Y., … Yabe, K. (2020). Extremely Metal-poor Representatives Explored by the Subaru Survey (EMPRESS). I. A Successful Machine-learning Selection of Metal-poor Galaxies and the Discovery of a Galaxy with M* < 106 M ⊙ and 0.016 Z ⊙. The Astrophysical Journal, 898(2), 142. https://doi.org/10.3847/15384357/aba047  Stephens, T. (2020). Powerful new AI technique detects and classifies galaxies in astronomy image data. Retrieved November 25, 2020, from https://news. ucsc.edu/2020/05/morpheus.html  Hosch, W. L. (2019, November 28). Supercomputer. Retrieved November 25, 2020, from http://www.britannica.com/ technology/supercomputer  HPC Basics Series: What is Supercomputing? (2019, March 11). Retrieved November 25, 2020, from http://www. nimbix.net/what-is-supercomputing  Peckham, O. (2020, July 24). Supercomputer Simulations Delve Into Ultra-Powerful Hypernovae. Retrieved November 25, 2020, from http://www. hpcwire.com/2020/07/23/supercomputer-simulations-delve-into-ultra-powerful-hypernovae/  Gough, E. (2020, July 21). Supercomputer Simulation Shows a Supernova 300 Days After it Explodes. Retrieved November 25, 2020, from http://www. universetoday.com/147096/supercomputer-simulation-shows-a-supernova-300-days-after-it-explodes.  C., H. (2020, September 25). Scientists May Have Developed New Way to Detect ‘Invisible’ Black Holes. Retrieved November 25, 2020, from http://www.sciencetimes.com/articles/26727/20200803/scientists-developed-new-way-detect-invsible-blackholes.htm  Penn State. (2020, August 3). Unequal neutron-star mergers create unique ‘bang’ in simulations. ScienceDaily. Retrieved November 24, 2020 from www.sciencedaily.com/releases/2020/08/200803184201.htm
Advancements in Aerospace Rutvik Marathe
Rocket science: just about one of the easiest subjects in the world. While we see launches becoming commonplace today, this wasn’t at all the case just about 100 years ago. That’s right - the venture of spaceflight is a very new one, requiring the most precise and powerful technologies we have ever made. It is far from easy; there are dozens of hurdles to overcome and situations to account for. The most notable of these challenges is due to Earth’s gravitational field, as lifting a rocket off the ground and sustaining its flight needs a lot of fuel. So much so, that roughly 80-90% of it is just fuel, preventing us from actually carrying much into space. If that wasn’t enough, the more fuel you carry, the more additional fuel you have to bring for that original fuel. However, even with challenging problems like these, society has made a lot of recent progress in launching things into space. From companies like SpaceX, Boeing, and Lockheed Martin, to government organizations like the Indian and Chinese space agencies, we have been overcoming the massive challenges that spaceflight presents by launching every few weeks. So how did we get to this point? Modern rocketry began to develop in the late 1800s and early 1900s. The development of aviation led to the first attempts to launch things off the ground, and using fuel propulsion came soon after. At this time, all flight was limited only to the Earth’s atmosphere. But these initial steps helped establish how rockets function (expelling something downwards to go up), and the process of enhancing them could start soon. From that point, early attempts to launch things into space were made. The first successful space launch came during the Cold War, when the Soviet Union launched the satellite Sputnik 1. This was huge news! A country had finally succeeded in putting something in orbit around the Earth. And although the R7 rocket that launched it was not very 44
powerful compared to modern standards, it was a monumental development in rocketry. Given the previously mentioned challenges, like the high fuel requirements for rockets, meant that it wasn’t as easy as “just making a bigger rocket” to launch heavier things. That approach would mean that we would need gigantic rockets much bigger than the ones we use today - to launch people and supplies into space. It was clear that we had to make advancements in the way the rocket was constructed and launched, rather than simply keep the current system, but make it larger. Significant advancements came during the time of the space shuttle, in the 1980s. New materials were being tested for structures like the fuel tank, most notably an aluminum lithium alloy. This new material reduced the rocket’s weight by about 20%, making launching and escaping Earth’s gravitational well easier, and allowing for greater payloads for missions. Another big advancement in this era was made in reusability. At first, rockets were designed to be one-time-use only, as recovery was too complicated a process to attempt early on. NASA’s Space Shuttle was the first breakthrough in this field, as it captured and reused the shuttle on many missions. Additionally, SpaceX has been a pioneer in creating reusable rocket boosters, which fly back to Earth and land on a platform in the middle of the ocean. Such technologies make space missions much cheaper and allow them to run quicker, as you don’t need to invest time and energy into remaking these parts of the rocket. Even with the current growth rate of technology, conventional propulsion (burning fuel like we do today) doesn’t seem like a long-term option if we want to expand past the Earth. Such movement beyond our own planet would need a high frequency of space missions, and therefore a lot of fuel. Not only is this too costly for any groups to carry out, it is also not a re-
NASA’s Evolutionary Xenon Thruster Project’s 7-kilowatt ion thruster. Source: NASA.gov.
Illustration of spacecraft powered by nuclear thermal propulsion. Source: NASA/Marshall.
newable energy source, so there is only a certain amount of it available for use . The resource-heavy launch process right now could be improved through alternative propulsion methods. These methods, if made efficient, could be the next big advancement which would allow us to travel to our solar system and beyond. One of these is electric propulsion - using electrical energy to shoot ions out of the rocket and making the rocket go forward via Newton’s third law. While the tiny mass of ions means that the thrusters produce very low acceleration, electricity would not be very hard to gather and mass produce. In fact, solar panels on a rocket could even “collect” fuel as the mission progresses! This type of clean energy for propulsion is being heavily researched and tested as a major source of fuel. Another approach being looked at is nuclear propulsion, where nuclear reactions in a rocket will burn hydrogen out of the end of the rocket, propelling it forward . This technology is also being developed right now as a possible “use” of all the nuclear bombs sitting idle in underground bunkers. Many agree that they would be put to better use in a rocket engine than being inactive (and potential apocalypse devices!). While these technologies are promising for the future, they don’t seem to be as powerful as the traditional methods of burning fuel. It is very likely that in the future, rockets use hybrid varieties of propulsion for different scenarios. For example, they could use traditional fuels for initial speed to leave the Earth’s surface, but electric or nuclear propulsion to navigate through space. There are many advancements that have been made in rocketry, and many more to come. Rocketry is catching speed as many private companies have joined the arena, competing to make the cheapest and most efficient rockets possible. For the next few decades, advancements in this field will only continue to grow, fueled by the uniting goal of expanding humanity past Earth! Citations  Bruno, T. (n.d.). The Properties of RP-1 and RP-2 MIPR F1SBAA8022G001. https://kinetics.nist.gov/RealFuels/macccr/macccr2008/Bruno2.pdf  6 Things You Should Know About Nuclear Thermal Propulsion. (2020b, January 21). Energy.Gov. https:// www.energy.gov/ne/articles/6-things-you-should-knowabout-nuclear-thermal-propulsion
Access to Space Carter Moyer
It was almost ten years ago when the Space Shuttle Atlantis touched down for the last time, bringing a close to the thirty-year-long program and the United States’ ability to send humans into space. Ever since, NASA and ESA have relied on the fourth generation Roscosmos’ tried and true Soyuz rocket—one that was far cheaper, older, and safer than the American-made shuttle. The past two Presidential administrations have pushed for that to change, initiating the commercial crew program and calling for the United States to once again partake in human spaceflight, and this time under the veil of neoliberal capitalism. The program has largely supported space-industry-veteran Boeing’s Starliner and astronautical-startup SpaceX’s Crew Dragon programs, but delay after delay, cost overrun after overrun, and an inflight safety test failure has cast a shadow on the program. And yet, in the middle of a global pandemic and mass mobilizations against systemic racism and anti-Blackness, something miraculous happened: SpaceX, not Boeing, safely ferried two astronauts to and from the International Space Station. There might have been a little booster landing thrown in there as well. This year has been full of space-related surprises and accomplishments, actually. NASA’s Mars 2020 Rover, now named Perseverance, and paired with its copter buddy, Ingenuity, are on their way to the red planet. This rover is uniquely poised to continue the goal of searching for life and surveying Mars—the lightweight aerial drone will be able to go to areas that Perseverance cannot, either due to geography or the sheer time it would take to get to them. The rover will also be leaving behind cached regolith samples in the hopes that future missions will be able to gather and study them. And the United States is far from the only active participant in space this year. China is hoping to launch its Chang’e-5 mission later this year, carrying a lander that will collect and then return lunar regolith samples to Earth, the first of its kind since the Soviet Union’s Luna 24 mission. The United Arab Emirates even had a Mars mission of its own, Hope, launched aboard a Japanese Mitsubishi rocket. All of these missions are, whether wholly or significantly, supported by governments with many also focusing on scientific discovery. There is also, undoubtedly, a large degree of national pride intertwined with these missions, but a motive that 46
is relatively absent is profit. The 2010s have been the launchpad for the 2020s’ space boom as nation states and multinationals alike pour money into fleeing a planet literally and metaphorically on fire. Governments will continue to launch scientific, exploratory, and, yes, vanity missions, but what we are increasingly seeing is the private sector taking up the monetization of space. It’s not a new concept nor is it an under-discussed one, but it is starting to come to fruition, specifically with SpaceX’s Starlink program. There are currently over five hundred SpaceX satellites in low Earth orbit, primed to offer internet connectivity to the public. With thousands of more satellites planned and the telecommunications industry handling trillions of dollars every year, SpaceX is primed to make a lot of money once these satellite constellations are operational. And with SpaceX also operating one of the only ways to get these satellites into LEO, the only other private corporations that could compete with them would be a pact between Amazon and Blue Origin. Enter Jeff Bezos’ Kuiper constellation. This seems to be a much more immediate, much more profitable way to monetize space compared to the oft-lauded space tourism industry which can only cater to a small number of high-net-worth individuals and has no room for error. Such an industry shift is also poised to redefine, or at the very least close the gap between, the roles of governments and corporations in space. For the longest time, only governments could fund and take on the risk of space exploration. It’s why the only consistent customers for sending people to the ISS are governments. Yet, many people are aware of Elon Musk’s plan to send people to Mars—it is the main mission of SpaceX, its prime directive. To do so would be inordinately risky and costly. Much like how Amazon Web Services is able to subsidize the rest of Amazon, however, Starlink may very well be the key ingredient in paving the way for Elon Musk’s billionaire space fantasies to become reality. The same applies to Jeff Bezos, Blue Origin, and Amazon. It’s far from the democratization of space once promised, but this decade will determine whether the keys to space remain exclusively in the hands of governments or are shared with the megarich. Citations  Wattles, J. (2019, November 16). Boeing and SpaceX face ‘significant’ challenges in delayed NASA program. https:// www.cnn.com/2019/11/16/tech/spacex-boeing-nasa-oig-scn/ index.html.  Smith-Schoenwalder, C. (2019, June 20). GAO: NASA Programs Rack Up Delays, Cost Overruns. U.S. News & World Report. https://www.usnews.com/news/national-news/articles/2019-06-20/gao-nasa-programs-rack-up-delays-costoverruns.  Sheetz, M. (2020, March 6). NASA investigation finds 61 corrective actions for Boeing after failed Starliner spacecraft mission. CNBC. https://www.cnbc.com/2020/03/06/
nasa-finds-61-corrective-actions-for-boeings-starliner-spacecraft.html.  Potter, S. (2020, July 30). NASA, ULA Launch Mars 2020 Perseverance Rover Mission to Red Planet. NASA. https://www.nasa.gov/press-release/nasa-ula-launchmars-2020-perseverance-rover-mission-to-red-planet.  Northon, K. (2018, May 11). Mars Helicopter to Fly on NASA’s Next Red Planet Rover Mission. NASA. https:// www.nasa.gov/press-release/mars-helicopter-to-fly-onnasa-s-next-red-planet-rover-mission.  Johnson, A., & Hautaluoma, G. (Eds.). (2020, June 17). The Extraordinary Sample-Gathering System of NASA’s Perseverance Mars Rover – NASA’s Mars Exploration Program. NASA. https://mars.nasa.gov/news/8682/the-extraordinary-sample-gathering-system-of-nasas-perseverance-mars-rover/.  Jones, A. (2020, August 6). On its way to Mars, Chinese spacecraft spots Earth and moon, aces steering maneuver. Space.com. https://www.space.com/china-marsmission-spots-earth-and-moon.html.  Bartels, M. (2020, July 19). United Arab Emirates launches ‘Hope’ mission to Mars on Japanese rocket. Space. com. https://www.space.com/hope-mars-mission-uaelaunch.html.  Etherington, D. (2020, July 15). Leak reveals details of SpaceX’s Starlink internet service beta program. TechCrunch. https://techcrunch.com/2020/07/15/leak-revealsdetails-of-spacexs-starlink-internet-service-beta-program/.  Sheetz, M. (2020, July 20). Morgan Stanley: SpaceX could be a $175 billion company if Elon Musk’s Starlink internet plan works. CNBC. https://www.cnbc. com/2020/07/20/morgan-stanley-spacex-could-be-175billion-company-if-elon-musks-starlink-works.html.  Grush, L. (2020, July 30). FCC approves Amazon’s internet-from-space Kuiper constellation of 3,236 satellites. The Verge. https://www.theverge.com/2020/7/30/21348768/ fcc-amazon-kuiper-satellite-constellation-approval.  Australian Associated Press. (2018, December 17). Richard Branson’s Virgin Galactic space flights criticised as ‘dangerous, dead-end tech’. The Guardian. https:// www.theguardian.com/science/2018/dec/18/richard-bransons-virgin-galactic-space-flights-criticised-as-dangerousdead-end-tech.  Sheetz, M. (2019, May 16). Elon Musk says SpaceX Starlink internet satellites are key to funding his Mars vision. CNBC. https://www.cnbc.com/2019/05/15/musk-on-starlink-internet-satellites-spacex-has-sufficient-capital.html.
Falcon 9 lifting off from the historic Launch Complex 39A, sending Crew Dragon to orbit on May 30, 2020. Source: SpaceX.
Figure 1: The Timeline of the Universe (NASA, 2006)
Understanding the Chronology of the Universe, from the Big Bang to the End of Time Andrew Tran Overview Understanding the past and future of our universe is an idea that cosmologists have worked on for several decades, tying into several big-picture, philosophical questions, such as “Why are we here?” or “What is the destiny of humanity in this vast universe?” Using theoretical models, calculations, and observations, physicists have been able to determine the stages and conditions that the universe has experienced from the Big Bang to today. From what astronomers have measured, it has also been possible to predict how the universe will look hundreds of billions of years into the future. After analyzing the densities of baryonic matter and dark energy, it has become known that the universe is expanding at an accelerated rate, and using this information allows for calculated inferences about the behavior of the universe throughout its chronology, going as far back as 13.7 billion years ago. The Moment of Creation The first stage in the timeline goes back to about 13.7 billion years ago, where it all began with the Big Bang. This moment is often referred to as the ‘Planck epoch’ or the ‘Grand unification epoch’, and marks a period of time that wasn’t even a microsecond long . When the universe was at this stage, all 48
of the four fundamental forces of nature, that being the three in the Standard Model (strong nuclear, weak nuclear, and electromagnetic), and gravity, were bonded together. The universe was extremely high in temperature, at around 1030 degrees Kelvin. A common misconception with the Big Bang is that it was an explosion that allowed the universe to exist à la Genesis, when really it was more like all of space expanding violently at once, increasing the distance between all of the structures in the universe that would eventually become galaxies and stars. The Big Bang truly marks the transition that the universe took from being barely a few millimeters across, to the cosmic size that we can see today . It is often denoted as the ‘birth’ of our universe because it’s where the fundamental ideas and laws of physics that we know today, such as general relativity and quantum mechanics, begin to work. This is where four fundamental forces of physics, that being the gravitational, strong nuclear, weak nuclear, and electromagnetic force began to break down, and separate. We have been able to validate and justify the Big Bang Theory, as it provides an explanation for many observations we’ve made, such as the Cosmic Microwave Background (CMB) and Hubble’s Law which indicates the expansion of the universe.
The Infant Universe Next is the period of time when the universe was only a few hundred thousand years old, just an infant compared to its age today. At this point, the scale of the cosmos had already begun to inflate. The tiny subatomic fluctuations within the fabric of the universe at this stage are speculated to have been the seeds for what would someday become galaxies.
them. The atoms no longer scatter light, so they can now travel freely, illuminating the stage of the cosmos. Atoms that were recently formed release photons that can still be detected today in the cosmic microwave background radiation, which is the furthest we can peer back in time into the cosmos—glimpses of the leftover radiation emitted during this era, at the microwave wavelength. The Dark Ages
Figure 2: Galaxies like NGC 4414 formed thanks to tiny quantum fluctuations. (NASA/ Hubble, 1999)
During infancy, the universe began to form several kinds of subatomic particles, which would someday be classified as quarks, leptons, and gauge bosons . From these subatomic particles, a large amount of matter and antimatter were formed, which annihilated one another whenever they interacted. However, the amount of matter just slightly exceeded the amount of antimatter, so that’s why today there’s only mostly matter in the universe today (though, if it was antimatter that was more abundant, we would have just ended up calling that matter anyways). About 1 second after the Big Bang, protons and neutrons (the essential building blocks of atoms) formed, and at around 2 minutes, collided, creating heavier elements such as deuterium . For about 50,000 years, the universe was too high in temperature for light to be able to travel, so it was just a cloudy, blurry plasma permeating everywhere. Eventually, the universe began to cool down, and began to be dictated by matter instead of radiation, forming the first molecules .
Unlike the Dark Ages following the fall of the Roman Empire, the Dark Ages refer to a time in the universe, lasting nearly a billion years, when the first stars and galaxies in the universe had yet to shine. The cosmos were making the transition from out of the “soup” of subatomic particles. What made the universe so “dark” at this time was that the light that could now travel freely was affected by the expansion of the universe, stretching out or red-shifting into wavelengths of light not in the visible spectrum. This darkness would end up lasting hundreds of millions of years. During the Dark Ages, the majority of the matter that occupied the universe included dark matter, and neutral, uncharged amounts of hydrogen and helium . Figure 4: An artistic representation of dark matter (Shutterstock) Eventually, the most ancient stars and galaxies began to form, due to the accumulation of baryonic (ordinary) matter and dark matter into disk-like structures. This point is commonly referred to as the “Epoch of Reionization” . Galaxy clusters would begin to form, slowly transitioning the universe out of the cosmic dark ages. The Present Day (Galaxy Era)
Figure 3: The Cosmic Microwave Background Radiation (NASA/WMAP, 2010) Over 300,000 years later, with temperatures much lower now, neutral atoms could be produced. This is the epoch known as “recombination.” Ionized atoms were formed as well, including hydrogen and helium, which are still the most abundant elements in the universe today. As we reach the end of the universe in its infancy, it starts to become transparent, since the ionized atoms have attracted electrons, neutralizing
After the dark ages, we’re brought to the present day, often referred to as the ‘galaxy era’ of the universe. Sometime into this stage, the Milky Way, then our solar system, and then the Earth entered the universe. And then just under 13.7 billion years following the Big Bang, the human race walked the Earth for the first time. If you were to scale down the entire history of the universe from the Big Bang until today into one calendar year, humans would have appeared just before midnight on New Year’s Eve. Figure 5: The golden age of our universe? (NASA/Hubble, 2003)
There is an estimated maximum of two trillion galaxies in the observable universe. Given our observations of the incoming light from galaxies, we have been able to conclude that the universe is expanding at an accelerating rate. The more matter and mass there is in an object, the more gravitationally attractive it will be, so one would expect that the combined masses of all the galaxies and groups of galaxies in the universe would result in everything collapsing in on one another. Since this isn’t the case, it means there is a mysterious force, which we still don’t know much about, pushing everything apart: dark energy. We have been able to conclude that dark energy makes up 68% of everything in the universe, dark matter makes up 27%, and normal, baryonic matter to be barely 5% . This makes sense since the universe can only accelerate if the density of matter is less than the density of dark energy. If the universe is expanding at an accelerating rate, that would mean that the galaxies are getting further apart from one another. Eventually, humanity will see fewer and fewer stars in the night sky. Our descendants, several thousands of years into the future, may not get to enjoy astronomy and stargazing as we get to today. Eventually, the last stars in the universe will dim, maybe explode in a supernova, but then eventually shut off for eternity. This brings us to the last stage in our timeline of the universe. What will be in store for existence as we know it? The Future and Fate of Our Universe The last stage of the universe timeline brings us to a point where the stars and galaxies begin to stop forming. The universe continues to expand at an accelerating rate, due to the effects of dark energy. Given current models and data that we have in cosmology, the most likely scenario that the universe will experience is the “Big Freeze,” in which the universe will keep expanding until it reaches a temperature of absolute zero. Some other theories, such as the “Big Rip” and the “Big Crunch” involve the universe going out in a spectacular and flashy way. But the one that seems to be our destiny is cold and silent. Eventually, once all of the stars have lived out their lives, all that will be left in the universe are black holes, constantly feeding on anything that gets near them, and maybe a few white dwarfs . It has been theorized that by this point protons will decay as well. The beautiful cosmos that we once knew will become a bizarre place mostly occupied by stellar corpses twisting and turning spacetime. During this period, black holes may merge together and release gravitational waves.
Figure 6: The universe will be dominated by these stellar predators. (NASA/JPL, 2013) 50
However, all things in the universe must come to an end, and this includes black holes. Due to the phenomenon known as Hawking radiation, black holes over time will evaporate as a result of the quantum effects near the event horizon, the boundary at the edge of the black hole, or “point of no return” where nothing may escape . Once the last black hole dies, the universe will see a glimmer of light one last time, when the last stellar remnant evaporates. Then, everything will go dark. Life will be unable to thrive in this universe anymore. The concept of time will become irrelevant. Perhaps it won’t be all bad, though. The last survivors, which may include humans, could find a way to escape this universe, and go to an entirely different one. Physicists have for many years postulated the idea of a multiverse, and if it’s true, then life—humanity, could live on to see another day. Citations  Adams, Fred C.; Laughlin, Gregory (1997). “A dying universe: the long-term fate and evolution of astrophysical objects”. Reviews of Modern Physics. 69 (2): 337–372. arXiv:astro-ph/9701131. Bibcode:1997RvMP...69..337A. doi:10.1103/ RevModPhys.69.337. S2CID 12173790.  Bridge, Mark (Director) (30 July 2014). First Second of the Big Bang. How The Universe Works. Silver Spring, MD. Science Channel.  Byrd, D. (2017, July 16). Peering toward the Cosmic Dark Ages. EarthSky. https://earthsky.org/space/cosmic-dark-ages-lyman-alpha-galaxies-lager  Chow, Tai L. (2008). Gravity, Black Holes, and the Very Early Universe: An Introduction to General Relativity and Cosmology. New York: Springer. ISBN 978-0-387-73629-7. LCCN 2007936678. OCLC 798281050.  Dark Energy, Dark Matter. (n.d.). Retrieved November 26, 2020, from https://science.nasa.gov/astrophysics/focus-areas/ what-is-dark-energy  First Light & Reionization - Webb/NASA. (n.d.). Retrieved November 26, 2020, from https://jwst.nasa.gov/content/science/firstLight.html  Kolb, Edward; Turner, Michael, eds. (1988). The Early Universe. Frontiers in Physics. 70. Redwood City, CA: Addison-Wesley. ISBN 978-0-201-11604-5. LCCN 87037440. OCLC 488800074.  Ryden, Barbara Sue (2003). Introduction to Cosmology. San Francisco: Addison-Wesley. ISBN 978-0-8053-8912-8. LCCN 2002013176. OCLC 1087978842.  WMAP Big Bang Theory. (n.d.). Retrieved November 26, 2020, from https://map.gsfc.nasa.gov/universe/bb_theory. html
2002 KM6 (99795) Naunet Leonhardes-Barboza single points of excited light sparkle the darkness a chill breeze in the latest hours of the night â&#x20AC;&#x201D;blink, there she is flying magnitudes brighter than her neighboring stars she still stands out a white dot among a sea of white dots
method of gauss Naunet Leonhardes-Barboza switch into gaussian time we stare at the whiteboard, exhausted
perhaps, similar to carl once did he looked at his desk contemplating questions for the universe
there are no limits, and he wants to find ceres ask your fellow intellect, like he did kepler laugh and cry when itâ&#x20AC;&#x2122;s over we now know where that small rock is going 51
tessellated constellations Alex Dong gazing upon the expanses of the great night sky our endless wonder at the mosaic sea of stars is inexplicably bridled by an intangible feeling that the abyss separating our world and distant realms forms a chasm of black emptiness meant to isolate different beings are we imprisoned on this earth destined to stay indefinitely watching other worlds pass by time and time again until time can no longer describe our passivity to their passing through a window of obscurity permitting only imagination to penetrate its dark tinted display of anonymous secrecy and us the victims of our own nature able to imagine but not to reach out and satisfy our insatiable curiosity we begin to realize the paradoxical plight of human existence as our seemingly powerful earthly dwelling is humbled by our temporary and insignificant presence in the vastness of the galaxy and the cosmos
so we are fated to be mighty yet powerless an existence woven in a tessellated fabric of captivating constellations and starry specks mirroring the very state of our planet our world our reality brimming with wonder and glimmering with hope
starry dreams Alex Dong stars dance around in twilight a few points on Nyxâ&#x20AC;&#x2122;s blanket incomprehensible yet sought to be understood but neither wonder nor courage can capture the essence of their immortal shine stars their luminescence a perpetual comfort their presence an eternal gaze yet their longevity a reminder that our time spent curiously probing is just a second in their eon their time is not ours and still we look on stars on the edge of the galaxy have already lived their lives and faded into oblivion our witness of their glory an elegy to the past time itself a warped cycle of dreamy slumber and starry imagination
images of the past Naunet Leonhardes-Barboza she can feed her own intellectual curiosity from the fruit of the delectable tree of life a red apple will fall upon her own head so she, too, can discover something she looks up at the night sky in awe unaffected by her immediate environment she, much like these glimmering dots is not yet disillusioned by the harsh realities of humanity but there was a time she almost gave in, she believed the stars were static they would never attempt to deceive her unlike the unforeseen friend or foe the illusion of simplistic dots the tempting twinkling as if saying “look at me closer” yet, they were clouded by natural gases in Earth’s air the stars can’t show her everything at once unless with aid for her human eyes she learned the truth about the lives of the stars she took images of the past millions of years ago she observed rainbows of color in the black void and the intensity of rocks flying through space she wondered if the stars would look back at her and dismiss her life as a dot, dim with little hope perhaps the stars and her feel the same way? wishing to project the light of the present and show each other their grand, dynamic journeys
unseen skies Alex Dong they say shoot for the moon even if you miss you’ll hit the stars but some shots fall short and the stars they never witness our dreams answer our prayers our shots ring unheard through the infinite darkness the unknown chasm oblivion yet we shoot and we work almost mindless in repetition almost mechanical in movement almost purposeless in routine we become an echo perpetuating the mechanism that bounds us to its cycle stifled in our own gasps for breath our spirits dim and flicker as gatsby’s green light winks out our dreams can only remain faraway fantasies forever but through pensive nights we gaze up to wisps of black and rays of dark in search of the place the purpose of our endless tunnel of toil these dark skies yield no answer yet somewhere within us we know that honest truth lies far beyond horizons we may perceive so when darkness falls at the thirteenth hour we shoot again hoping one day someday we’ll reach the unseen skies 55
Astrophotography Details Ryan Caputo - Tulip Nebula (Sh2-101) Dates: July 4, 2020, July 5, 2020, July 8, 2020 Imaging and Guiding: Guan Sheng Optical Classical Cassegrain 6” F/12 Mount: iOptron CEM60 Imaging camera: ZWO ASI1600MM. Guiding camera: ZWO ASI 290mm Mini Editing Programs and Techniques: Software: N.I.N.A , PixInsight 1.8 Ripely. Filters: Radian Triad-Ultra Ryan Caputo - Hercules Galaxy Cluster (Abell 2151) Dates: April 28, 2020, May 18, 2020, May 29, 2020, May 30, 2020 Imaging and Guiding: Guan Sheng Optical Classical Cassegrain 6” F/12 Mount: iOptron CEM60 Imaging camera: ZWO ASI1600MM. Guiding camera: ZWO ASI 290mm Mini Editing Programs and Techniques: Software: N.I.N.A , PixInsight 1.8 Ripely. Wilson Zheng - Horsehead Nebula (Barnard 33) Date: March 27, 2020 Location: Dixon Observatory, Berkshire School, MA, USA Equipment: Mead LX200 GPS 14” f/10 Camera Details: ZWO ASI1600MC Acquisition Details: 27 @ 60 seconds Editing Programs and Techniques: Processed with PIXINSIGHT Wilson Zheng - Messier 3 (near Bootes) Date: April 16, 2020 Location: Dixon Observatory, Berkshire School, MA, USA Equipment: Mead LX200 GPS 14” f/10 Camera Details: ZWO ASI1600MC Acquisition Details: 240 @ 30 seconds Editing Programs and Techniques: Processed with PIXINSIGHT Wilson Zheng - Sunflower Galaxy (Messier 63) Date: March 27, 2020 Location: Dixon Observatory, Berkshire School, MA, USA Equipment: Mead LX200 GPS 14” f/10 Camera Details: ZWO ASI1600MC Acquisition Details: 360 @ 30 seconds Editing Programs and Techniques: Processed with PIXINSIGHT
Anavi Uppal - Star Trails over Pierson College Date: August 26, 2020 Location: Pierson College at Yale University Camera: Nikon D500 Lens: Rokinon 10mm F2.8 Ultra Wide Angle Lens Sky: 84 images @ f/2.8, 10mm, ISO 1600, 30 sec. Foreground: 1 image @ f/2.8, 10mm, ISO 1600, 1.6 sec Anavi Uppal - Comet NEOWISE (Wide) Date: July 19, 2020 Location: Orlando, Florida Camera: Nikon D500. Lens: Nikon AF-P DX NIKKOR 18-55mm f/3.5-5.6G VR Acquisition Details: 12 images @ f/5.6, 55mm, ISO 1600, 6 sec
Anavi Uppal - Comet NEOWISE (Zoomed) Date: July 19, 2020 Location: Orlando, Florida Camera: Nikon D500. Lens: Nikon AF-P DX NIKKOR 18-55mm f/3.5-5.6G VR Acquisition Details: 12 images @ f/5.6, 22mm, ISO 1600, 8 sec Owen Mitchell - Comet NEOWISE Date: mid-July 2020 Location: Bozeman, Montana Equipment: William Optics Redcat 51 Camera Details: Canon SL2 at 250mm on an i Optron SkyGuider Pro Acquisition Details: Stacked exposures worth 2 minutes Owen Mitchell - Milky Way Galaxy Date: Summer 2019 Location: Etscorn Campus Observatory at New Mexico Tech Camera Details: Canon SL2 with a tiki on 14mm lens on an iOptron SkyGuider Pro Acquisition Details: 25 seconds Nathan Sunbury - The Ring Nebula (Messier 57) Date: Summer 2016 Location: Sommers-Bausch Oservatory, University of Colorado Boulder Nathan Sunbury - The Moon Date: Summer 2016 Location: Sommers-Bausch Observatory, University of Colorado Boulder
Cameron Woo - Pleiades (Messier 45) Date: January 20, 2020 Location: New Jersey Camera Details: AF-S Nikkor 55-300mm f/4.5-5.6G ED DX lens at 300mm, f/5.6, and 1 second Acquisition Details: 160 light frames at ISO 6400, 18 darks, 22 bias, and 18 flats Software: stacking performed in SiriL in MacOS, editing performed in Affinity Photo Cameron Woo - The Summer Triangle Asterism Date: August 11, 2020 Location: New Jersey Camera and Acquisition Details: Rokinon 16mm f/2.0 lens for crop sensor Nikon cameras at f/4.0, 25 seconds, ISO 3200 Additional Details: 130 light frames, 20 darks, 20 bias, and 18 flats Software: stacking performed in Deep Sky Stacker on Windows 10, editing performed in Affinity Photo Cameron Woo - Milky Way Galaxy Date: August 30, 2016 Location: Kaanapali in Maui in bortle 3-4 skies Camera Details: Nikon AF-S Nikkor 35mm f/1.8G DX lens Acquisition Details: f/1.8, at ISO 800 or 1600 for 5-8 seconds Software: frames stacked in Deep Sky Stacker on Windows 10, edited in Affinity Photo
Jonah Rolfness - Sadr Region Mount: Ioptron IEQ30 Pro Camera: ASI1600mm Filters: Astronomik 6nm Ha, OIII Optics: Rokinon 85mm F1.4 lens Acquisition Details: 600x120sec Ha shot at F1.4 234x120sec OIII shot at F1.4 Preprocessing: WBPP Script used to calibrate all lights with darks, flats, and flat darks SubframeSelector used to apply various weights to each frame StarAlignment used to register each channel ImageIntegration used to create masters for each channel, ESD Rejection Algorithm Postprocessing: DynamicCrop and alignment of each master channel DBE to remove gradients Linear Fit channels MLT Noise Reduction on each channel Ha very aggressively stretched by STF to increase contrast OIII given normal STF stretch Pixelmath used to combine to a HOO palette Various curve transformations on saturation and RGB brightness LHE and MLT Bias applied for sharpening Aggressive star reduction applied using starnet and morphological transformation Clonestamp tool used to salvage crescent and tulip nebulas after starnet ate them DarkStructureEnhance Script
Cameron Woo - The Orion Nebula (Messier 42) Date: late December 2019 Location: Bergen County suburbs 30 minutes from Manhattan in bortle 8 skies Camera Details: AF-S Nikkor 55-300mm f/4.5-5.6G ED DX lens at 300mm, f/5.6, 1 or 1.3 seconds Acquisition Details: 183 frames at ISO 3200, 70 frames at ISO 6400, 100 at ISO 12800 Additional Details: 14 flat frames, 36 dark frames, 42 bias frames Software: stacking performed in SiriL in MacOS, editing performed in Affinity Photo
Jonah Rolfness - The Orion Nebula (Messier 42) and Running Man Nebula (Sh2-279) Mount: Ioptron IEQ30 Pro Camera: ASI1600MMC Filters: ZWO LRBG, Astronomik Ha, SII, OIII Telescope: GSO 6in F/5 Newtonian Reflector Autoguider: QHY5L-II-M paired with the Orion 60mm guide scope Acquisition Details: Panel 1: -40x5, 41x60, 38x300 Ha -52x120 R -50x120 G -48x120 B Panel 2: -38x5, 41x60, 42x300 Ha -51x120 R -50x120 G -49x120 B Software: PIXINSIGHT Kappa Sigma Stacking in DSS to deal with geo sats HDR combine on Ha stacks for HDR Luminance MMT Noise Reduction Histogram stretch -HDR Transformation to bring the core to proper levels -Color Combination -Curve Transformations to bring out red colors, lower background RBG, and saturation -Luminance addition -ACNDR in post-linear state to further reduce noise -SCNR to reduce green hue -Star Reduction
Jonah Rolfness - The Pinwheel Galaxy (Messier 101) Mount: Ioptron IEQ30 Pro Camera: ASI1600MMC Filters: Ha/SII/OIII Astronomik 6nm and ZWO LRGB Telescope: GSO 6in F/5 Newtonian Reflector Autoguider: QHY5L-II-M paired with the Orion 60mm guide scope Acquisition Details: Roughly 800x120sec Luminance 200x120sec RGB 450x120sec Ha Preprocessing: Stacked and calibrated using DSS Kappa Sigma Clipping Postprocessing: DynamicCrop and alignment of each master channel DBE to remove gradients Deconvolution script applied to bring out detail in galaxy core MLT Noise Reduction on each channel LRGB Combination to create final master with STF stretch Separate STF for Ha channel Various curve transformations on RGB brightness and saturation Added in Ha layer using pixelmath to brighten the red nebula in the galaxy Jonah Rolfness - The Heart Nebula (IC 1805) - Fish Head Nebula (IC 1795) and Melotte 15 Mosaic Mount: Ioptron IEQ30 Pro Camera: ASI1600MMC Filters: Ha/SII/OIII Astronomik 6nm Telescope: GSO 6in F/5 Newtonian Reflector Autoguider: QHY5L-II-M paired with the Orion 60mm guide scope Acquisition Details: Panel 1: 300x12, 1800x3 Ha; 300x11 OIII; 300x10 SII Panel 2: 300x21, 1800x4 Ha; 300x24 OIII; 300x20 SII Panel 3: 300x22, 1800x4 Ha; 300x22 OIII; 300x16 SII Panel 4: 300x23, 1800x4 Ha; 300x24 OIII; 300x25 SII Processing- Stacking: Used DSS to create master lights for short Ha, long Ha, OIII, and SII Stacked both long and short Ha to create a master Ha light. Appropriate darks and flats were used Pixinsight: DynamicCrop and DBE on all 12 stacks(Ha, OIII, SII) StarAlignment used to create a rough mosaic for each filter GradientMergeMosiac and DNALinearFit used to create a final mosaic for each filter. Noise reduction using MultiscaleLinearTransform applied for each master frame. ChannelCombination used to create a master SHO image. SCNR green applied for both regular and inverted(magenta star reduction) Many different curve transformations, boosting saturation, shifting hue, and reducing RBG background levels. TGVDenoise and ACNDR applied to further eliminate noise
Educational Opportunities Scholarships The Science Ambassador Scholarship: A full–tuition scholarship for a woman in science, technology, engineering, or math. Funded by Cards Against Humanity. Open to high school and undergraduate students. Applications close December 14th, 2020 at 11:59PM CST. Richard Holland Memorial Scholarship: Open to high school and undergraduate students. Applications will be accepted online starting January 1, 2021 through March 15, 2021. The Gladys Carol Scholarship Program, Inc: Open to high school seniors, high school graduates, and current undergraduate level students who are United States citizens or permanent residents. Application process opens on January 1, 2021. SBB Research Group STEM Scholarship: Available to currently enrolled full-time students pursuing a STEM degree. Awarded on a quarterly basis in 2021; the next application deadline is February 28, 2021. CC Bank’s Young Scholars Scholarship: Each year CC Bank’s Young Scholars Scholarship offers up to five $2,000 scholarships to students attending universities, colleges, and other academic institutions across the U.S. Applicants must apply by Thursday, December 31, 2020, to get the scholarship during the 2021-2022 academic year. Lockheed Martin STEM Scholarship: For high school seniors and current college freshmen or sophomores. Women in Aerospace Scholarship Women in Technology Scholarship (WITS) Program Google Scholarships for Computer Science Annual Collabera STEM Scholarship Regeneron Science Talent Search The Gates Scholarship
Programs and Internships Summer Science Program in Astrophysics: Open to rising juniors and seniors in high school. Applications open on December 15, 2020. California State Summer School for Mathematics and Science (COSMOS): Open to students in grades 8-12. Applications due in early 2021. NASA SEES: Open to high school students. Applications due in early 2021. Yale Summer Program in Astrophysics (YSPA): Open to rising high school seniors. On temporary hiatus for 2021, will reopen in 2022. Jet Propulsion Laboratory Summer Internship Program: Open to undergraduate and graduate students pursuing degrees in STEM fields. Applications due on March 31, 2021. Boston University Research in Science & Engineering Program (BU RISE): Open to rising high school seniors. Applications open on December 15, 2020. Research Science Institute by the Center for Excellence in Education (RSI): Open to rising high school seniors. Applications due on January 16, 2021. Science Internship Program at UC Santa Cruz Research Mentorship Program at UC Santa Barbara Kopernik Observatory & Science Center High School Internship Program Alfred University Astronomy High School Institute Space Telescope Science Institute Space Astronomy Summer Program APL Johns Hopkins STEM Academy
International Opportunities Work Experience at the Australian National University: The observatory offers a limited number of work experience places to year 10, 11 and 12 students each year. These placements are typically 1 week in duration and the students work on an astronomical project under the supervision of professional astronomers. International Astronomy Summer Internship Program: The summer internship is designed for pupils in the final years of high school, or those who have just finished high school. During their three-week stay, participants work on a variety of astrophysical observations and experiments.
Contributor Biographies Priti Rangnekar is a freshman at Stanford University, majoring in computer science and engineering physics. She has researched asteroid orbit determination at the 2019 Summer Science Program, conducted a datadriven astrophysics Senior Project at BASIS Independent Silicon Valley, and analyzes exoplanet transits through a collaborative project with AAVSO observers. As a 7-time national qualifier and Tournament of Champions quarterfinalist in speech and debate, as well as a scientific writer for student journals, she champions the value of logical thinking and effective communication in a variety of fields. She has received recognition in international competitions for computing and business, and she enjoys conducting STEM outreach. She seeks to connect fellow students around the world while fostering knowledge and hands-on exploration of the universe in an inclusive, engaging community. Rutvik Marathe is a freshman at the University of Michigan, majoring in aerospace engineering. An ardent space enthusiast, Rutvik has conducted asteroid orbit determination research at the Summer Science Program in 2019, pursues astrophotography, and has independently studied topics in orbital mechanics and chaos theory. In addition to space-related endeavors, he has earned recognition at the state level in varsity debate and competitive mathematics, as well as at the national level in programming. With expertise in team leadership and teaching STEM, he strives to promote curiosity and interest for the universe and space exploration through SkyShot. Naunet Leonhardes-Barboza is a young Costa RicanAmerican woman planning to major in astrophysics and computer science at Wellesley College. She has experience volunteering for The Rockland Astronomy Club and for Girls’ World Expo as a Birght Ideas/Science Coordinator. An alum of the 2019 Summer Science Program in Astrophysics, she has learned and researched in both the Stull Observatory and Sommers-Bausch Observatory. She loves spending my free time writing poetry about space and further exploring her interest in astronomy through all mediums. Victoria Lu is a freshman at Yale University. She is prospectively double majoring in art and evolutionary biology. She is an alum of the 2019 Summer Science Program in Astrophysics. Victoria seeks to connect global communities on contemporary issues, such as climate change and conservation, through scientific research and education. 60
Carter Moyer is a freshman at Harvey Mudd College, majoring in engineering and political science. He is also an alum of the 2019 Summer Science Program in Astrophysics. Vighnesh Nagpal is a freshman at UC Berkeley hoping to pursue a double major in Physics and Astrophysics. He is fascinated by everything ‘space’, having gained experience doing research as part of the Summer Science Program in Astrophysics and his experiences working with scientists at Caltech on exoplanet research. He hopes to continue exploring and learning about the exciting state of astronomy today. Ezgi Zeren is a freshman at Tufts University, majoring in mechanical engineering. She hails from Istanbul, Turkey. Although it is difficult to see the stars and all other celestial objects from Istanbul’s crowded streets, she considers herself lucky to participate in SSP at CU Boulder in 2019. “At the Sommers-Bausch Observatory, I looked through a telescope first time in my life. Since then, I try to share what I explore looking at the sky with others, who want to see and know more.”
Anavi Uppal is a freshman at Yale University who plans on double majoring in astrophysics and computer science. She is an alum of the 2019 Yale Summer Program in Astrophysics, where she researched the newly discovered supernova SN 2019hyk. She was an intern at the 2018 NASA STEM Enhancement in Earth Science (SEES) Program, where she helped design a lunar habitat and lunar mission. Anavi greatly enjoys participating in astronomy outreach, and hopes to inspire others to see the beauty in our universe. She often volunteers at astronomy nights and occasionally gives talks to the public. Anavi has been doing astrophotography for five years, and specializes in landscape astrophotography. Her work can be viewed on Instagram at @anaviuppal.
Alexandra Masegian is a second-year student at the University of Chicago, where she is majoring in astrophysics and minoring in data science and creative writing. Her primary interests lie in stellar and extragalactic astrophysics, and she has been an astrophysics research intern at the University of California, Santa Cruz, the American Museum of Natural History, and Fermi National Accelerator Laboratory. She is also an alum of the 2018 NASA STEM Enhancement in Earth Science (SEES) Program, where she was a member of the Explore the Moon team. Alex is passionate about science communication and outreach, and hopes to spend her career broadening humanity’s knowledge of the vast and beautiful universe we live in. Her work can be found in SkyShot itself, as well as in The Spectrum, a UChicago-based e-publication about science and society. Andrew Tran is a second-year student at the University of Georgia majoring in astrophysics and minoring in mathematics. He has been involved in many facets of the astrophysics community, as a former NASA intern, as an undergraduate researcher in the Department of Physics and Astronomy at his school, and as the creator and founder of Astrophysics Feed, a science media page on Instagram (@astrophysicsfeed). In his spare time, Andrew likes astrophotography, reading books about space or physics, and learning anything about the world and universe.
Feli is a high school senior from Ohio interested in astronomy and astrophysics. They are an alumnus of the 2020 Summer Science Program in Astrophysics, where they tracked the path of a near-Earth asteroid. In their free time, they enjoy reading, stargazing, and learning more about space! Ryan Caputo is a freshman at the University of Colorado Boulder. Originally from Texas, he is an alum of the 2019 Summer Science Program in Astrophysics. Alex Dong is a first-year student at Yale from Canada, having recently graduated with a bilingual high school diploma and the AP International Diploma accolade. He is also an alum of the 2019 Summer Science Program in Biochemistry. At Yale, he is planning to major in Molecular Biophysics and Biochemistry, although his passion for poetry—especially themed around astronomy—will continue to occupy his spare time.
Sofia Fausone is from Northern California. She is currently a first year at Yale and hopes to double major in physics and math. She’s especially interested in theoretical physics and is excited to explore different areas of each field. Timothy Hein is a Computer Engineering Major at Purdue University. Though his passions include technical design, coffee, and classical literature, he plans on pursuing a career in early stage venture capital. Abby Kinney is a freshman at Williams College interested in studying physics. Originally from Washington, she is an alum of the 2019 Summer Science Program in Astrophysics. In her free time, she enjoys observing the night sky.
Owen Mitchell is a college freshman at Johns Hopkins University. Originally from Montana, he is an alum of the 2019 Summer Science Program in Astrophysics at New Mexico Tech. Jonah Rolfness is a college freshman at the California Institute of Technology. Originally from Arizona, he is an alum of the 2019 Summer Science Program in Astrophysics at New Mexico Tech.
Nathan Sunbury is a senior at Harvey Mudd College hailing from California. He is an alum of the 2016 Summer Science Program at the University of Colorado Boulder.
Sydney Wang is from Dalian, China and went to the Webb Schools. He currently studies physics at The University of Pennsylvania. Cameron Woo is a freshman at the University of Pennsylvania in the School of Engineering and Applied Science. Originally from New Jersey, he is an alum of the 2019 Summer Science Program in Astrophysics at the University of Colorado Boulder. Wilson Zheng hails from Shanghai, China and a high school senior at Berkshire School. He is also an alum of the 2020 Summer Science Program in Astrophysics.