Welcome to the DiRAC Institute newsletter, winter edition!

One quarter into the new academic year, I’m excited to share with you new additions to DiRAC’s team, and some of the exciting work and discoveries made by our researchers.

We are very pleased to welcome two new DiRAC Postdoctoral Fellows, Azalee Bostroem and Pedro Bernardinelli. Azalee brings us the exciting world of supernovae research and applications of data science in astronomy, whereas Pedro’s work takes us to the outskirts of the Solar System in search of new (dwarf) planets and comets. You can read more about their research in this newsletter.

Read further about remarkable new machine-learning driven “supernova recognizer” by Kyle Boon. In his recently published paper Kyle introduced a new statistical model called ParSNIP, which can be distinguish different types of supernovae significantly better than other state-of-the-art methods. Kyle’s code is open source, ready to be used by researchers in the community.

Finally, we will tell you about a major, multi-year collaboration we started with Carnegie Mellon on astronomical software for LSST, co-led by our Prof. Andy Connolly and generously funded by Schmidt Futures. Through this work, a team of a dozen software engineers and scientists will work to create new software platforms to analyze extremely large astronomical datasets. These types of systems will allow us to truly harness the data from the upcoming Legacy Survey of Space and Time, and map and understand the structure of our Universe.

Mario Jurić

Director, DiRAC Institute
Professor, Department of Astronomy

Azalee (pronounced “OZ-a-lee”) is really excited to be in Seattle and joining DiRAC. She comes from a non-traditional career path, majoring in Mathematics at Vassar college where she also was certified to teach middle and high school math. A detour from those plans took her to San Diego State University for a master’s degree in astronomy – where she fell in love with the field.

She developed a strong background in programming and data analysis as a research and instrument analyst at the Space Telescope Science Institute – where she supported the Cosmic Origins Spectrograph and the Space Telescope Imaging Spectrograph, two instruments on the Hubble Space Telescope. With the goal of returning to the west coast, she left the Space Telescope Science Institute to pursue a PhD in Physics at University of California, Davis where she studied hydrogen-rich supernovae with Prof. Stefano Valenti.

Hydrogen-rich supernovae are produced when the iron cores of stars between eight and thirty times the mass of the sun collapse and produce a neutron star. Infalling material bounces off the neutron star creating a shockwave which unbinds the star in a really bright explosion we call a supernova. While entire fields of study are devoted to studying massive stars and supernovae, we are still trying to figure out how to connect our observations of supernovae to their massive star progenitors. Two aspects of this connection that Azalee is particularly interested in are using supernovae to understand how massive stars lose mass just prior to explosion and what mass stars explode as hydrogen-rich supernovae. I use supernova observations to understand progenitor mass and mass loss by modeling the light curves of supernovae, observing them at X-ray and radio wavelengths, and by modeling the spectra one to two years after explosion.

While these technique worked great for the tens of supernovae we were discovering per year a few decades ago, they have not scaled well to the thousands of supernovae we are discovering with current surveys and will be unusable on most of the hundreds of thousands supernovae discovered by the Rubin Observatory’s Legacy Survey of Space and Time (LSST). Azalee’s current focus is on building tools to model the hydrogen-rich supernova light curves produced by the LSST to measure the progenitor properties of hundreds of thousands of massive stars in the final stages of their lifetimes. 

In addition to supernova research, Azalee is actively involved with the Carpentries organization which teaches best practices for programming and data analysis to scientists in an open and inclusive way. She has been a certified instructor since 2012 and has been organizing workshops at the winter meeting of the American Astronomical Society since 2014. She is currently leading the development of a new Data Carpentry curriculum for astronomy called Foundations of Astronomical Data Science which teaches fundamental astronomy and data science skills such as working with databases and tables and communicating results through a compelling visualization. 

On the weekends you can find her exploring Seattle by bike, hiking in the mountains, or paddling around Lake Union on her paddle board. She spends her less active days baking and cuddling with her two cats.

Read more on Azalee’s Website and follow on Twitter, Github, ADS Publications.

Pedro Bernardinelli was born in São Paulo, Brazil and completed his undergraduate studies in Physics at the University of São Paulo. After that, he got his Ph.D from University of Pennsylvania, focusing on the development and application of new techniques for the discovery and characterization of the most distant bodies in our Solar System, trans-Neptunian objects, as a member of the Dark Energy Survey (DES).

As part of this research, Pedro has led the discovery of over 600 TNOs and the comet C/2014 UN271 (Bernardinelli-Bernstein), the largest Oort-cloud comet ever found. His research also has had deep applications to the Planet 9 hypothesis, as well as to current models of the trans-Neptunian region.

At the University of Washington, Pedro is excited to expand this research to current surveys, as well as upcoming projects such as the DECam Ecliptic Exploration Project (DEEP) and the Rubin Observatory’s Legacy Survey of Space and Time (LSST).

Pedro is also generally interested in astronomical data analysis and image reduction techniques, going from precise astrometry and photometry to detection of faint sources. Before the pandemic started, Pedro was one of the hosts and organizers of Astronomy on Tap Philly.

Outside academia, Pedro is interested in photography, fantasy/sci-fi literature, board and video games, cooking, baking, and is a great coffee enthusiast.

Read more on Pedro’s Website, and follow Twitter,  Github, ADS Publications.

DiRAC researchers are heavily involved in building the Vera C. Rubin Observatory, a new facility that is currently under construction in Chile. This observatory will feature the 8.4 meter Simonyi Survey Telescope and the world’s largest CCD camera which will scan the entire visible sky every three nights. It will discover and observe millions of supernovae which are powerful explosions of stars that can outshine an entire galaxy for a brief period of time.

A particular type of supernovae called “Type Ia” can be used to map out how the universe has expanded since the big bang. This led to the discovery of dark energy which was awarded the Nobel Prize in 2011. The Rubin Observatory will discover over 100 times as many Type Ia supernovae then have been observed by all surveys to date and will dramatically improve our understanding of the universe.

Extracting scientific results from this large deluge of data is a big challenge. In a paper that was recently published in the Astronomical Journal, DiRAC Fellow Kyle Boone discusses a new statistical model called ParSNIP that can be used to distinguish Type Ia supernovae from others and improve our maps of the universe. This novel work combines recent advances in computer science and deep learning with physics models of how light propagates through the universe. The resulting hybrid model is the first one that can empirically describe how the emitted light spectrum from any kind of supernova evolves over time.

This foundational work has many applications. ParSNIP will be used to identify the different kinds of supernovae that the Rubin Observatory finds, and it can do this with over twice the performance of previous models. It will also be used to hunt for new unknown kinds of supernovae in the large Rubin dataset. ParSNIP will use all of the millions of supernovae that the Rubin Observatory discovers to measure the properties of dark energy in contrast to current methods that can only use less than a tenth of the full sample. This work will transform supernova science with the Rubin Observatory and help to extract the full scientific potential.

ADS Publication: Published October, 2021, ParSNIP: Parametrization of SuperNova Intrinsic Properties

About

Kyle Boone is DiRAC Postdoctoral Fellow. Kyle’s research focuses on developing novel statistical methods for astronomy and cosmology. He is particularly interested in using Type Ia supernovae to probe the accelerated expansion of the universe that we believe is due to some form of “dark energy”. One aspect of his research focuses on identifying Type Ia supernovae among the millions of astronomical transients that upcoming astronomical surveys such as the Large Synoptic Survey Telescope (LSST) will discover.

Read more here. GitHub here.

Trans-Neptunian objects provide a window into the history of the solar system, but they can be challenging to observe due to their distance from the Sun and relatively low brightness.

In the recently published paper, Sifting through the Static: Moving Object Detection in Difference Images, DiRAC researchers report the detection of 75 moving objects that could not be linked to any other known objects, the faintest of which has a VR magnitude of 25.02 ± 0.93 using the Kernel-Based Moving Object Detection (KBMOD) platform.

They recover an additional 24 sources with previously known orbits and place constraints on the barycentric distance, inclination, and longitude of ascending node of these objects. The unidentified objects have a median barycentric distance of 41.28 au, placing them in the outer solar system. The observed inclination and magnitude distribution of all detected objects is consistent with previously published KBO distributions. They describe extensions to KBMOD, including a robust percentile-based lightcurve filter, an in-line graphics-processing unit filter, new coadded stamp generation, and a convolutional neural network stamp filter, which allow KBMOD to take advantage of difference images.

These enhancements mark a significant improvement in the readiness of KBMOD for deployment on future big data surveys such as LSST.

ADS Published Paper access here.

As we come to a close of a challenging but scientifically exciting academic year, I’m delighted to share in this newsletter some of the work and discoveries made by DiRAC researchers over the past months. 

We start with a profile of Dr. Stephen Portillo, a DiRAC Postdoctoral Fellow whose work at the intersection of statistics, machine learning, and astronomy is making it possible for us to precisely measure even the densest areas of our Galaxy. Then read about how Dr. Kyle Boone, a DiRAC and NSF Postdoctoral Fellow, uses “supernova twins” for precision cosmology — precise measurements of distances in the universe. Find out how Joachim Moeyens, one of our graduate students, is advancing the state-of-the-art in discovery of dwarf planets, comets, and asteroids in the Solar System with novel object discovery algorithms. And finally, stay for an interview with DiRAC’s associate director Prof. Jim Davenport about the searches for intelligent life in the universe, and tale of a rare eclipsing binary system, RR Hydrae. 

These are just some of the many accomplishments our researchers made in a year marked by the stresses of the pandemic and remote work. I am especially proud by how we’ve pulled through this difficult times by supporting and caring each other, and through it all managed to push forward the boundaries of science. As we move into the summer and plan for return to campus in the fall, I can’t help but be excited by the prospect of our entire DiRAC community being in person, together, again!

Mario Jurić

Professor, Department of Astronomy
Director, DiRAC Institute

Stephen Portillo’s research focuses on using advances in statistics and machine learning to allow more science to be done with existing astronomical data sets. On the statistics front, he has been developing probabilistic cataloging, a Bayesian Markov chain Monte Carlo method that improves source detection and measurement in crowded images. On the machine learning front, he has been applying autoencoders, a type of deep neural network, to enable astronomers to more easily find patterns and outliers in large datasets.

Dr. Portillo is a DiRAC Postdoctoral Fellow and UW Data Science Postdoctoral Fellow. He joined the DiRAC Institute in September 2018 after finishing his PhD in Astronomy and Astrophysics at Harvard University. Before graduate studies, he completed a BSc in Astrophysics at the University of Alberta in Edmonton, Canada in 2012.

Crowded images are difficult to analyze because sources can appear blended with their close neighbors. This problem will worsen with more sensitive observatories like Rubin Observatory, James Webb Space Telescope, and Roman Space Telescope that will see unprecedented numbers of objects in the same area of sky. Unlike traditional methods that first identify sources before measuring them, probabilistic cataloging treats source identification probabilistically. Dr. Portillo has shown that this method can find stars four times fainter than state-of-the-art methods in extremely crowded images with 1 star per 10 pixels.

At DiRAC, Dr. Portillo has joined the KBMOD team, who are developing GPU-accelerated software to find Kuiper belt objects. Recently, he has been developing a method to correct for the Earth’s motion in the solar system, allowing KBMOD to better track objects over longer periods of time. He is also extending probabilistic cataloging to search for binaries among the objects found by KBMOD, because these binaries are powerful probes of the dynamical history of the outer Solar System.

Working with Prof. Connolly, Dr. Portillo has implemented a variational autoencoder to galaxy spectra from the Sloan Digital Sky Survey. He showed that the autoencoder can summarize spectra with thousands of pixels with only six numbers and easily separates known classes of galaxies. Currently, he is working with students to use this autoencoder to find massive black hole binaries, rare objects identified by unusual spectra.

Dr. Portillo is also passionate about public outreach and teaching. While at DiRAC, he has given an Astronomy at Home talk and given virtual presentations to school groups. He was also a lecturer at AstroHackWeek 2020 and is currently co-instructing an undergraduate course on astrostatistics with Prof. Juric.

Dr. Portillo is excited to be at the DiRAC Institute because it brings together researchers interested in all aspects of data-intensive science from software engineering to statistics and machine learning. He is also happy to be a part of the eScience Institute that encourages researchers across scientific fields to find commonalities in the ways they analyze data.

A team led by Dr. James Davenport, research assistant professor of astronomy at the UW and associate director of the UW’s DIRAC Institute, analyzed more than 125 years of observations of HS Hydra – from astro-photographic plates in the late 1800s to 2019 observations by TESS – and showed how this system has changed dramatically over the course of just a few generations. The two stars began to eclipse in small amounts starting around a century ago, increasing to almost full eclipses by the 1960s. The degree of eclipsing then plummeted over the course of just a half century, and will cease around February 2021.

Read the full article here.

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Astronomers document the rise and fall of a rarely observed stellar dance

The sun is the only star in our system. But many of the points of light in our night sky are not as lonely. By some estimates, more than three-quarters of all stars exist as binaries — with one companion — or in even more complex relationships. Stars in close quarters can have dramatic impacts on their neighbors. They can strip material from one another, merge or twist each other’s movements through the cosmos.

And sometimes those changes unfold over the course of a few generations.

That is what a team of astronomers from the University of Washington, Western Washington University and the University of California, Irvine discovered when they analyzed more than 125 years of astronomical observations of a nearby stellar binary called HS Hydrae. This system is what’s known as an eclipsing binary: From Earth, the two stars appear to pass over one another — or eclipse one another — as they orbit a shared center of gravity. The eclipses cause the amount of light emitted by the binary to dim periodically.

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Continue reading this article by James Urton in the UW press release here.

One of the significant research focuses at the DiRAC Institute has been the development of next generation asteroid and comet discovery algorithms. DiRAC researchers have published a pre-print detailing one such algorithm called “Tracklet-less Heliocentric Orbit Recovery” (THOR). Applied to observations from the Zwicky Transient Facility (ZTF), THOR recovered 97% of the known objects with at least 5 observations, a factor of 1.5-2 more than the current generation of discovery algorithms. In addition to recovering most of the known objects, THOR would have discovered nearly 500 new Solar System objects (including a parabolic/hyperbolic comet) had it been running when the observations were made in 2018. 

The Hammer and the Comet 

One of the significant research focuses at the DiRAC Institute has been the development of next generation asteroid and comet discovery algorithms. DiRAC researchers have published a pre-print detailing one such algorithm called “Tracklet-less Heliocentric Orbit Recovery” (THOR). Discovering minor planets involves having current generation astronomical surveys observe the same area of the sky at least twice in one night. The two sets of observations of the same region of the sky can then be scanned for what is known as a “tracklet”: a motion vector made of at least two observations that could represent the actual motion of a Solar System object. This observing pattern is repeated over the course of a 2-week window until enough tracklets are observed so that they can be used to discover asteroids and comets. 

Requiring tracklets for Solar System discovery has two striking consequences: first, any astronomical survey with the goal of discovering minor planets must observe the same area of sky at least twice in one night thereby limiting the amount of sky the telescope could observe in a single night. Second, any dataset that was not constructed with a tracklet building cadence is not a dataset suitable for Solar System discovery. Leveraging the latest innovations in Solar System discovery and backed by large scale computing, THOR has addressed these concerns by removing the requirement for tracklets to be made and enabling minor planet discovery without the need for a specific cadence of observations. 

As a proof-of-concept demonstration, THOR was applied to two weeks of observations from the Zwicky Transient Facility (ZTF) that were taken in early September 2018. THOR recovered 97% of all Solar System objects known at the time. ZTF’s own discovery algorithm, ZMODE, which relies on tracklet-like observations for discovery, could at best recover 68% of the same population. The Vera C. Rubin Observatory’s discovery algorithm which also relies on tracklets would at best recover 45% of the same population. In other words, by enabling Solar System discovery without requiring a specific pattern of observations, THOR can recover 1.5-2 times as many asteroids and comets as the current generation of algorithms. Of the 21,000+ orbits that were recovered by THOR, 488 were identified as high quality discovery candidates that could not be associated with any objects that were known in 2018. 

DiRAC researchers then posed the question: had THOR been running on ZTF when the data were taken in 2018, how many asteroids and comets would it have discovered? Of the 488 discovery candidates identified, THOR would have discovered at least 477 new asteroids and comets had it been running as ZTF’s discovery algorithm when the observations were made. 

The observations and best-fit trajectories of the 11 remaining objects are shown in the figure. Subsequent analysis showed that of the 11 candidates, 10 are as yet undiscovered objects. The e > 1 candidate (2nd row, 3rd column) represents “precovery” observations of parabolic/hyperbolic comet C/2018 U1 that was discovered on October 27th 2018 by the Mount Lemmon Survey. Precovery observations are observations of an object that pre-date its original discovery date. The ZTF data on which THOR was tested and developed were taken 6-8 weeks prior to the discovery date of this comet. Had THOR been running in September 2018, it would have allowed ZTF to claim the discovery of this fascinating object. 

Following their success using just two weeks of ZTF observations, the research team behind THOR is now working to process all three years of ZTF observations. They anticipate this should yield the discovery of several hundred new Solar System objects. THOR is completely open-source and available on GitHub. The eventual goal of the THOR project is to launch a discovery service where surveys can submit their observations and, powered by THOR, they can be processed and analyzed for the discovery of new asteroids and comets.

Joachim Moeyens is a graduate student in the Department of Astronomy at the University of Washington. He is interested in big data and software driven solutions to problems in astronomy. During his undergraduate studies at the University of Washington, he was presented with the opportunity to work on a research project for the Vera Rubin Observatory’s Legacy Survey of Space and Time (LSST).  For his doctoral thesis, Joachim is working on algorithms that discover minor planets in astronomical surveys, in particular, on Rubin Observatory’s Solar System Processing pipelines, and on a novel algorithm named Tracklet-less Heliocentric Orbit Recovery (THOR).