BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//LBNL Physics Division Research Progress Meetings - ECPv6.8.3//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://rpm.physics.lbl.gov
X-WR-CALDESC:Events for LBNL Physics Division Research Progress Meetings
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:UTC
BEGIN:STANDARD
TZOFFSETFROM:+0000
TZOFFSETTO:+0000
TZNAME:UTC
DTSTART:20220101T000000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=UTC:20221129T131000
DTEND;TZID=UTC:20221129T141000
DTSTAMP:20260421T123948
CREATED:20221124T014607Z
LAST-MODIFIED:20221125T174038Z
UID:2039-1669727400-1669731000@rpm.physics.lbl.gov
SUMMARY:SPEAKER: Kate Storey-Fisher - New York University - TITLE: Galaxy Clustering with Emulation and Equivariant Machine Learning
DESCRIPTION:This is a Virtual Event \nLocation: Zoom from campus In Campbell Hall\, Room 131\n \nTITLE: Galaxy Clustering with Emulation and Equivariant Machine Learning \nABSTRACT: \n\nThere is significant untapped cosmological information in the clustering of galaxies\, particularly at small scales. I will discuss two projects aimed at this. In the Aemulus project we take an emulation approach\, populating dark matter only cosmological simulations with a simple galaxy–halo connection model and training machine learning (ML) emulators to predict clustering statistics. I will show that incorporating beyond-standard statistics sensitive to the local environment aids in constraining galaxy bias parameters and increases the precision on recovered cosmological parameters. \nTo account for uncertainties in galaxy formation\, we require improved models of the galaxy–halo connection. I will present a new equivariant ML approach to learning the relationship between dark matter halo and galaxy properties in cosmological simulations. Our approach explicitly respects physical symmetries by describing halos in terms of a large set of invariant dimensionless scalars. I will show that this results in precise predictions of galaxy properties. These frameworks will be critical for the analysis of upcoming spectroscopic surveys. \n\n\nZOOM Information – \n\nJoin Zoom Meeting\nhttps://lbnl.zoom.us/j/91782268585
URL:https://rpm.physics.lbl.gov/event/speaker-kate-storey-fisher-new-york-university-title-galaxy-clustering-with-emulation-and-equivariant-machine-learning/
LOCATION:HYBRID 50A-5132 (Sessler Conference Room)\, https://lbnl.zoom.us/j/91782268585\, 50A-5132
ORGANIZER;CN="Patrick McDonald":MAILTO:pvmcdonald@lbl.gov
END:VEVENT
END:VCALENDAR