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SUMMARY:Machine Learning In Astrophysics:  Estimating Galactic Baryonic Pr
 operties from Their Dark Matter
DTSTART;VALUE=DATE-TIME:20191107T061000Z
DTEND;VALUE=DATE-TIME:20191107T063000Z
DTSTAMP;VALUE=DATE-TIME:20260724T113242Z
UID:indico-contribution-9@sshep.snu.ac.kr
DESCRIPTION:Speakers: Ji-hoon Kim (SNU)\nAs astrophysics and cosmology dea
 l with inherently "cosmological" sizes of data\, machine learning is being
  rapidly adopted in a variety of astrophysical applications.  In this talk
 \, I will introduce a pipeline that estimates baryonic (visible) propertie
 s of a galaxy based purely on dark matter (DM\; invisible) properties in l
 arge-scale DM-only simulations.  It is shown that our pipeline promptly ge
 nerates a galaxy catalogue from a DM halo catalogue using a machine traine
 d on smaller-scale\, fully-hydrodynamic\, high-resolution simulations. An 
 extremely randomized tree algorithm is used together with multiple novel i
 mprovements we developed such as a refined error function and two-stage le
 arning.  Our model may become a promising method to transplant the baryon 
 physics of galaxy-scale hydrodynamic calculations onto a larger-volume DM-
 only run.\n\nhttps://sshep.snu.ac.kr/event/107/contributions/9/
LOCATION:Seoul National University\, College of Natural Sciences\, Buildin
 g 25-1 국제회의실 (International Conference Hall)
URL:https://sshep.snu.ac.kr/event/107/contributions/9/
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