In Person in Marlar and/or Virtual Brown Bag Lunch
Monday October 17, 2022 at 12:00 Link Below
Join BBL Zoom Meeting
Presentation in Marlar 37-252/37-272 for those wishing to attend in person
Yuhan Yao, CalTech at 12:05 (in person )
The X-ray Bright Tidal Disruption Event AT2021ehb
Abstract: Tidal disruption events (TDEs) provide ideal laboratories to study the real-time formation of an accretion disk around a massive black hole and the subsequent disk evolution. Over the past few years, time domain sky surveys such as the optical Zwicky Transient Facility and the Spektrum-Roentgen-Gamma X-ray satellite have led to a resurgence of TDE discoveries. In this talk, I will highlight how detailed multi-wavelength studies of the nearby TDE AT2021ehb have helped us understand super-Eddington accretion. Our joint NICER+NuSTAR observations show X-ray spectral features characteristic of relativistic disk reflection for the first time in a non-jetted TDE. I will end by describing new opportunities of TDE studies offered by upcoming time domain experiments throughout the electromagnetic spectrum.
Yuhan is a fifth-year graduate student at Caltech working on high energy transients, including tidal disruption events, supernovae, X-ray binaries, etc. She uses ZTF and SRG/eROSITA as the discovery engines, and conducts follow-up observations with multi-wavelength facilities. She’s currently interested in inferring local black hole demographics with TDE sample studies.
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Adriana Dropulic, Princeton at 12:30 pm (in person)
Revealing the Milky Way’s Most Recent Major Merger with a Gaia EDR3 Catalog of Machine-Learned Line-of-Sight Velocities
Abstract: Machine learning can play a powerful role in inferring missing line-of-sight velocities from astrometry in surveys such as Gaia. We apply a neural network to Gaia Early Data Release 3 (EDR3) and obtain line-of-sight velocities and associated uncertainties for ~92 million stars. The network, which takes as input a star’s parallax, angular coordinates, and proper motions, is trained and validated on ~6.4 million stars in Gaia with complete phase-space information. The network’s uncertainty on its velocity prediction is a key aspect of its design; by properly convolving these uncertainties with the inferred velocities, we obtain accurate stellar kinematic distributions. As a first science application, we use the new network-completed catalog to identify candidate stars that belong to the Milky Way’s most recent major merger, Gaia-Sausage-Enceladus (GSE). We present the kinematic, energy, angular momentum, and spatial distributions of the ~450,000 GSE candidates in this sample, and also study the chemical abundances of those with cross matches to GALAH and APOGEE. The network’s predictive power will only continue to improve with future Gaia data releases as the training set of stars with complete phase-space information grows. This work provides a first demonstration of how to use machine learning to exploit high-dimensional correlations on data to infer line-of-sight velocities, and offers a template for how to train, validate and apply such a neural network when complete observational data is not available.
I am a current 4th year graduate student in the particle phenomenology group at Princeton. I am interested in astrophysical probes of new physics, particularly in using information from galactic dynamics to constrain theories of dark matter. I am also excited to apply novel machine learning techniques to these studies.