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SUMMARY:Inferring Spatial Distributions of Dark Matter in Dwarf Galaxies w
 ith ML
DTSTART;VALUE=DATE-TIME:20261028T180500Z
DTEND;VALUE=DATE-TIME:20261028T181500Z
DTSTAMP;VALUE=DATE-TIME:20261023T011205Z
UID:indico-contribution-3656@nyx.physics.mcgill.ca
DESCRIPTION:Speakers: Chun On (Andy) Yu (University of Toronto)\nThe spati
 al distribution of dark matter can help us understand the formation and ev
 olution of the structures in our universe. However\, dark matter is invisi
 ble\, so we need to infer its distribution from the motions of visible sta
 rs. Previous techniques struggle to handle incomplete and biased datasets\
 , such as missing velocity information\, and have difficulty incorporating
  measurement uncertainties. To mitigate these limitations\, we develop Neu
 ral Likelihood Estimator (NLE)\, a machine learning framework which models
  the probability of seeing a given set of visible stars given a set of dar
 k matter parameters. By applying Bayes’ theorem\, we can recover the dar
 k matter distribution of the galaxy from visible stars. Our model not only
  performs well on synthetic galaxies\, but it has proven to outperform tra
 ditional methods and modern techniques at recovering the distributions of 
 dark matter from real observational data of the dwarf galaxy Draco. The me
 thod is also highly effective at handling uncertainty and incomplete and b
 iased data.\n\nhttps://nyx.physics.mcgill.ca/event/587/contributions/3656/
LOCATION:Online 
URL:https://nyx.physics.mcgill.ca/event/587/contributions/3656/
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