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SUMMARY:Bayesian anomaly detection for Cosmology - 21cm\, Supernovae\, and
  beyond - Samuel Leeney (University of Cambridge)
DTSTART:20250527T101500Z
DTEND:20250527T110000Z
UID:TALK229936@talks.cam.ac.uk
CONTACT:Charles Walker
DESCRIPTION:We introduce a unified Bayesian anomaly-detection framework fo
 r Cosmology\, applied to the REACH global 21cm probe and also Type Ia supe
 rnovae. This approach embeds data-integrity beliefs directly into the infe
 rence process. Rather than excising contaminated or anomalous data points\
 , the method employs a piecewise likelihood constrained by a Bernoulli pri
 or and an Occam penalty\, allowing anomalies to be down-weighted automatic
 ally while performing numerical sampling for parameter inference. When app
 lied to supernova light curves\, the framework yields precise estimates of
  brightness scaling\, stretch\, and colour\, while also automating superno
 va sample and band selection. In the context of global 21 cm cosmology\, i
 t offers a principled way to mitigate radio-frequency interference (RFI)\,
  particularly within the band of interest. We also discuss additional pote
 ntial applications of this methodology.
LOCATION:Martin Ryle Seminar Room\, Kavli Institute
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