BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Instituto de Física Teórica - ECPv6.15.11//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Instituto de Física Teórica
X-ORIGINAL-URL:https://www.ift.uam-csic.es
X-WR-CALDESC:Events for Instituto de Física Teórica
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Europe/Madrid
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20240331T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20241027T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20250330T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20251026T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20260329T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20261025T010000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Europe/Madrid:20251106T110000
DTEND;TZID=Europe/Madrid:20251106T120000
DTSTAMP:20260407T154202
CREATED:20251029T142950Z
LAST-MODIFIED:20251125T081102Z
UID:23524-1762426800-1762430400@www.ift.uam-csic.es
SUMMARY:Machine Learning Journal Club: 'Hierarchical Simulation-Based Inference of Supernova Physics'
DESCRIPTION:Speaker: Alex Gagliano (IAIFI/MIT) \nVenue&Time: 11:00 / Grey Room 2 \nAbsrtact: Supernovae are powered by diverse physical mechanisms such as radioactive decay\, circumstellar interaction\, and magnetar spin-down\, but distinguishing among them from light curves alone remains a major challenge. In this talk\, I’ll present a hierarchical simulation-based inference framework that jointly infers both the dominant power source and the key physical parameters of supernovae. By training conditional neural density estimators end-to-end across multiple physical models\, this approach yields calibrated posteriors even in regions of strong degeneracy. I’ll highlight the model’s performance on synthetic data and outline plans to fine-tune the model for population-level studies of diverse explosion physics.
URL:https://www.ift.uam-csic.es/event/machine-learning-journal-club/
LOCATION:Grey Room 2\, Instituto de Física Teórica\, Madrid\, Madrid\, 28037\, Spain
CATEGORIES:Journal clubs,Scientific activities
END:VEVENT
END:VCALENDAR