From Tens to Tens of Thousands: Supernovae Science in the Big-Data Era
| Date/Time: | Tuesday, 02 Apr 2019 from 4:10 pm to 5:00 pm |
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| Location: | Phys 0003 |
| Phone: | 515-294-5441 |
| Channel: | College of Liberal Arts and Sciences |
| Actions: | Download iCal/vCal | Email Reminder |
Abstract: Despite observations of thousands of type Ia supernovae (SNe Ia), we still do not have a clear understanding of the progenitor systems of these cosmic explosions. Our limited understanding of these events restricts our understanding of the composition of the Universe, and the nature of Dark Energy. The most promising path to understanding the progenitor physics is obtaining observations of the SNe Ia within a few days of the explosion. Modeling these early observations can provide us vital clues to distinguish between different theoretical models of SNe Ia, and a window into the nature of the deaths of stars. They also allow us to improve our Bayesian methods to determine distances to SNe Ia, and thereby constrain the equation of state of the Dark Energy, w. I will discuss SN2018oh and other spectroscopically confirmed SNe Ia with exceptionally early-time observations, discovered by the Kepler Extragalactic Survey (KEGS), and the implications the exquisite K2 light curves have for different SNe Ia progenitor scenarios. While events with such early observations are exceedingly rare, each provides an invaluable piece of the puzzle. To scale from tens to tens of thousands of objects, we must rapidly follow-up new events from wide-field ground-based surveys. I'll discuss work to use cutting edge data science and deep learning techniques to identify these, and other multi-messenger astrophysical phenomena in real-time within the Zwicky Transient Facility (ZTF). I will highlight how we're preparing for LIGO's O3 gravitational wave campaign, and some of the interesting sources we've already identified within ZTF with the ANTARES system that is being commissioned. Finally, I'll outline how we're preparing the community to jump scale from the current generation of surveys such as ZTF, to the massive LSST (Large Synoptic Survey Telescope).
Bio: I am the Barry M. Lasker Data Science Fellow at the Space Telescope Science Institute. I am an an active member of the Large Synoptic Survey Telescope science collaborations, focused on studying time-domain astrophysical phenomena, the nature of Dark Energy, and the photometric calibration of the entire survey. I finished my Ph.D. at Harvard in 2013, on measuring the equation of state of the dark energy using type Ia supernovae from the ESSENCE and Pan-STARRS surveys. I did post-graduate work at the National Optical Astronomy Observatory in Tucson, establishing high-precision spectrophotometric standards for future surveys, and developing the ANTARES alert broker system. At STScI, I work on finding explosive transients with the Kepler and TESS satellites, designing deep learning systems for LSST, and refining the algorithms used to determine distances from SNe Ia. When I'm not working, I'm active in local education and outreach efforts, or off on a hike, looking for fossils, or photographing the Appalachian.