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SUMMARY:BEAMS with Bias Correction: Is It Well-Calibrated?
DTSTART;VALUE=DATE-TIME:20261028T182100Z
DTEND;VALUE=DATE-TIME:20261028T182200Z
DTSTAMP;VALUE=DATE-TIME:20261024T225351Z
UID:indico-contribution-3696@nyx.physics.mcgill.ca
DESCRIPTION:Speakers: Linh Vo ()\nType Ia supernovae (SNe Ia) are importan
 t standardizable candles used to measure cosmic distances and constrain th
 e expansion rate of the universe. A central challenge in analyzing these t
 ransients is Malmquist bias\, which the Bayesian Estimation Applied to Mul
 tiple Species (BEAMS) with Bias Correction (BBC) framework aims to correct
 . While BBC yields unbiased cosmological constraints for small samples\, i
 ts underlying BiasCor lookup table may fail to maintain performance as sam
 ple sizes scale up significantly.\nIn this work\, we evaluate the calibrat
 ion of the BBC framework using backward simulation-based inference. We sim
 ulate surveys containing 2\,000\, 5\,000\, and 20\,000 pure SNe Ia under t
 he precise cosmological model assumed by the BBC BiasCor table. Passing th
 ese samples through the BBC pipeline\, we perform Simulation-Based Calibra
 tion (SBC) to test whether the framework is properly calibrated.\nEven wit
 hin this idealized setting – no sample contamination and model misspecif
 ication – we show that the BiasCor component introduces systematic overc
 onfidence that worsens as sample size increases. Furthermore\, the BiasCor
  correction interacts with the parameter degeneracy to underestimate uncer
 tainty specifically along that axis rather than uniformly. As next-generat
 ion surveys like the Vera C. Rubin Observatory dramatically scale up SNe I
 a datasets\, these findings highlight important limitations in the current
  BBC pipeline and motivate the development of scalable and fully calibrate
 d frameworks.\n\nhttps://nyx.physics.mcgill.ca/event/587/contributions/369
 6/
LOCATION:Online 
URL:https://nyx.physics.mcgill.ca/event/587/contributions/3696/
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