Bayesian Methods for Inferring Missing Data in the BATSE Catalog of Short Gamma-Ray Bursts

Abstract

The knowledge of the redshifts of Short-duration Gamma-Ray Bursts (SGRBs) is essential for constraining their cosmic rates and thereby the rates of related astrophysical phenomena, particularly Gravitational Wave Radiation (GWR) events. Many of the events detected by gamma-ray observatories (e.g., BATSE, Fermi, and Swift) lack experimentally measured redshifts. To remedy this, we present and discuss a generic data-driven probabilistic modeling framework to infer the unknown redshifts of SGRBs in the BATSE catalog. We further explain how the proposed probabilistic modeling technique can be applied to newer catalogs of SGRBs and other astronomical surveys to infer the missing data in the catalogs.

New detailed characterization of the residual luminescence emitted by the GAGG:Ce scintillator crystals for the HERMES Pathfinder mission

“…The knowledge of redshift can also aid in identifying the host galaxy of the SGRB, providing further understanding of the progenitor system and the environment in which the event occurred. [231] propose a bayesian methodology to estimate the redshift of SGRBs based on their observed features. This approach involves a semi-Bayesian data-driven technique, beginning with the segregation of SGRBs and LGRBs using the fuzzy clustering algorithm, applied to a larger sample of GRBs.…”