Supernovae are the explosions of stars that can be seen across vast distances, appearing as new bright points of light in optical images of the sky, even when the original star was far too faint to be detected.

When different types of stars explode (e.g., low-mass and high-mass) they cause supernovae with a variety of characteristics (e.g., brightness, color, duration). When two or more supernovae (explosions of stars) occur in the same galaxy, we say they have the same “parent galaxy” and are “sibling supernovae”. The characteristics of sibling supernovae can thus be compared knowing that, since they have the same distance from Earth and come from similar environments, any differences between them are more likely to be related to the type of star that exploded. Sibling supernovae are thus very useful for obtaining a better understanding of both supernovae and their parent galaxies.

Since the average supernova rate for a Milky Way-type galaxy is just one per century, a large imaging survey is required to discover an appreciable sample of sibling supernovae. In this paper we present 10 sibling supernovae in 5 parent galaxies from the wide-field Zwicky Transient Facility (ZTF).

For each of these families we analyze the supernova’s location within the parent galaxy, finding agreement with expectations that supernovae from more massive stars are found nearer to their parent galaxy’s core, and in regions of more active star formation.

We also present an analysis of the relative rates of core collapse and thermonuclear sibling supernovae, finding a significantly lower ratio than past samples due to the unbiased nature of the ZTF.

Published paper by Melissa Graham

ADS: Supernova Siblings and their Parent Galaxies in the Zwicky Transient Facility Bright Transient Survey

About

Melissa Graham currently works for Rubin Observatory as the Lead Community Scientist for the Community Engagement Team and as a Science Analyst for the Data Management team. Her main research focus is supernovae, especially those of Type Ia.

Space urgently needs special legal protection similar to that given to land, sea and atmosphere to protect its fragile environment, argues a team of scientists. The scientific, economic and cultural benefits of space should be considered against the damaging environmental impacts posed by an influx of space debris — roughly 60 miles above Earth’s surface — fueled by the rapid growth of so-called satellite mega-constellations.

In a paper published April 22 in Nature Astronomy, the authors assert that space is an important environment to preserve on behalf of professional astronomers, amateur stargazers and Indigenous peoples.

“We need all hands on deck to address the rapidly changing satellite situation if we can hope to co-create a future with dark and quiet skies for everyone,” said co-author Meredith Rawls, a research scientist with the Vera C. Rubin Observatory and the University of Washington’s Data Intensive Research in Astrophysics and Cosmology Institute, or DiRAC Institute.

Read UW News article in full here.

The flashing of a nearby star drew the attention of a team of astronomers, who discovered that it is part of a rare and mysterious system. As they report in a paper published May 4 in Nature, the stellar oddity appears to be a “black widow binary” — a type of system consisting of a rapidly spinning neutron star, or pulsar, that is circling and slowly consuming a smaller companion star, as its arachnid namesake does to its mate.

The team, led by co-author Kevin Burdge, a postdoctoral researcher at the Massachusetts Institute of Technology, found the black widow binary utilizing data from the Zwicky Transient Facility, a California-based observatory that takes wide-field images of the night sky.

“This discovery highlights the potential of large time-domain surveys like ZTF to find rare astrophysical objects,” said co-author Eric Bellm, a research assistant professor of astronomy at the University of Washington, fellow at the UW’s DiRAC Institute and scientist with both the ZTF and the Chile-based Vera C. Rubin Observatory.

Read full UW Press Release here.

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.