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

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.

Automated summary

The knowledge of redshifts of Short-duration Gamma-Ray Bursts (SGRBs) is crucial for understanding cosmic rates and related phenomena. This study presents a generic data-driven probabilistic modeling framework to infer the unknown redshifts of SGRBs and provides insights on applying this technique to other astronomical surveys.