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DTSTART;TZID=Europe/Madrid:20251105T150000
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DTSTAMP:20260720T231618
CREATED:20251029T142819Z
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UID:23522-1762354800-1762358400@www.ift.uam-csic.es
SUMMARY:Machine Learning for Time-Domain Astrophysics
DESCRIPTION:Speaker: Alex Gagliano (IAIFI/MIT) \nVenue&Time: Blue Room / 3:00 PM \nAbstract: The time-evolving night sky is rich with variable stars\, supernovae\, and merging neutron stars. Wide-field imaging surveys that monitor this variability produce gappy\, multi-modal observations that demand scalable\, uncertainty-aware models for physical inference. In this talk\, I’ll survey my recent work in building machine learning methods for time-domain astrophysics\, with a focus on learning representations of our data for classification\, physical inference and the discovery of astrophysical anomalies. I’ll introduce Minuet\, a compact host-galaxy image encoder trained with diffusion modeling; and a mixture-of-experts model that fuses supernova light curves and spectra while preserving modality-specific information and yielding calibrated posteriors. I’ll conclude by outlining three areas at this intersection with the greatest potential to drive discovery in the coming years: better physical models\, scalable population studies\, and ML-guided survey optimization. \nThis seminar is related to the I+D+i project whose reference is CEX2020-001007-S\, funded by MCIN/AEI/10.13039/501100011033. The topic is framed within the following research line: Origin and composition of the universe: Astroparticles and Cosmology (Astro/Cosmo).
URL:https://www.ift.uam-csic.es/event/seminar-by-alex-gagliano-iaifi-mit/
LOCATION:Blue Room\, Instituto de Física Teórica (IFT) - C. Nicolás Cabrera\, 13-15\, Fuencarral-El Pardo\, Madrid\, 28049\,\, Spain
CATEGORIES:Scientific activities,Seminars
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