Paris workshop on Bayesian Deep Learning for Cosmology and Time Domain Astrophysics 3rd ed.

Session

Time domain astrophysics

22 mai 2025, 14:00

Buffon Amphitheater (Université Paris Cité)

22. Do androids dream of exploding stars and receding galaxies?

federica bianco (NYU)

22/05/2025 14:00

Time Domain Astrophysics

Talk

14. Bayesian Neural networks and supernova classification

Anais Moller (Swinburne University)

22/05/2025 15:20

Talk

keynote talk + discussion session

26. Accounting for Selection Effects in Supernova Cosmology with Simulation-Based Inference and Hierarchical Bayesian Modelling

Benjamin Boyd (University of Cambridge)

22/05/2025 16:20

Talk

Type Ia supernovae (SNe Ia) are thermonuclear exploding stars that can be used to put constraints on the nature of our universe. One challenge with population analyses of SNe Ia is Malmquist bias, where we preferentially observe the brighter SNe due to limitations of our telescopes. If untreated, this bias can propagate through to our posteriors on cosmological parameters. In this work, we...

28. Classification of Transient Event Candidates in LSST Without Human-Labeled Training Sets

Bruno Sanchez (CPPM - CNRS)

22/05/2025 16:40

Talk

The Vera C. Rubin Observatory’s LSST will detect millions of transient candidates through difference image analysis (DIA), issuing real-time alerts to the community. While DIA is highly sensitive, it is prone to spurious detections caused by noise, artifacts, or imperfect subtractions. Filtering these out—known as the "Real/Bogus" problem—typically relies on supervised machine learning trained...