Thursday, 28 October 2021

Astronomy Colloquium (5.31/2021):Dark matter, black holes, and completely disrupted satellites -- Shadow of the Milky Way

05.31.2021 2:00 pm - 3:00 pm


Title: Dark matter, black holes, and completely disrupted satellites -- Shadow of the Milky Way
ESA’s astrometric satellite Gaia provides accurate kinematic data for a billion stars in the Milky Way. I will talk about how the Gaia data enabled us to do various dynamical inferences about the structure and history of the Milky Way. 
First, I will talk about the 3D dark matter density profile of the Milky Way estimated from the distribution function modelling of the kinematics of halo RR Lyrae stars. Our analysis strongly disfavors oblate dark matter halo models. 
Secondly, I will discuss the usefulness of high-velocity stars moving in the halo. I will talk about (i) an old population of high-velocity stars; (ii) a hyper-runaway star (LAMOST-HVS1) that might have been ejected by an intermediate-mass black hole; and (iii) a new usage of hyper-velocity stars ejected by the Galactic supermassive black hole to measure the Solar velocity. 
Lastly, if time allows, I will briefly talk about a clustering analysis of chemically peculiar stars to find remnants of completely disrupted dwarf galaxies. 
Kohei Hattori got his B.S. degree in 2009 and Ph.D. in 2014, both at the University of Tokyo. As a postdoctoral fellow, he worked at the University of Cambridge for two years, the University of Michigan for three years, and Carnegie Mellon University for one year. In September 2020, he joined the National Astronomical Observatory of Japan and the Institute of Statistical Mathematics (Tokyo) as a tenure-track assistant professor. He has been mainly working on the stellar dynamics in the Milky Way, and his main research interest in stellar dynamics includes the Galactic halo, bar, stellar disk, stellar streams, and hyper-velocity stars. Triggered by the advent of Gaia data, he has also started working on astrostatistics. His research interest in astrostatistics includes efficient Bayesian analysis, anomaly detection, sparse modeling, and clustering analysis with noisy data.
Time:14:00-15:00, 31/May, Monday
Meeting ID: 831 4360 9634
Passcode: 436377
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