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SUMMARY:Siddharth Mishra-Sharma (MIT)
DTSTART:20211019T183000Z
DTEND:20211019T193000Z
DTSTAMP:20260423T005732Z
UID:nhetc/27
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/nhetc/27/">U
 sing machine learning to look for dark matter in the Galactic Center\, the
  Milky Way halo\, and other galaxies</a>\nby Siddharth Mishra-Sharma (MIT)
  as part of NHETC Seminar\n\n\nAbstract\nAdvancements in machine learning 
 have enabled new ways of doing inference on forward models defined through
  complex\, high-dimensional simulations. After briefly motivating their us
 e in the cosmological context\, I will present applications of these simul
 ation-based inference methods to three separate astrophysical systems with
  the goal of looking for signatures of dark matter. First\, I will describ
 e how they can be used to combine information from thousands of strong gra
 vitational lensing systems in a principled way in order to extract the pop
 ulation properties of dark matter. Then\, I will quantify their sensitivit
 y to the collective imprint of dark matter subhalos in our own Galaxy on t
 he measured motions of background luminous celestial objects. Finally\, I 
 will present an application to the Galactic Center gamma-ray excess\, wher
 e the fact that our method can extract more information from the gamma-ray
  dataset than is possible with traditional techniques can be used to more 
 robustly characterize the nature of the excess and constrain its dark matt
 er properties.\n
LOCATION:https://researchseminars.org/talk/nhetc/27/
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