Work

Hierarchical Bayesian Inference on the Intrinsic Event Rate of Type I Gamma-Ray Bursts from the Fermi/GBM Catalog

Year: 2026

Type: article

Abstract: The intrinsic event-rate of Type I gamma-ray bursts (GRBs) as a function of redshift, provides a probe for the binary neutron star (BNS) merger population. However, the abovementioned inference is complicated by instrumental selection effects, the lack of redshift measurements for the majority of GRBs, and uncertainties in jet geometry. In this work, we applied a hierarchical Bayesian inference framework to the Fermi/GBM Type I GRB catalog to reconstruct the underlying event-rate population. This framework accounts for the selection effects and marginalizes over the limited-information posteriors of individual GRB redshifts and jet structures. The population model uses the parameterized luminosity function, distribution of jet structure parameters, the Madau-–Dickinson star formation rate and a delay-time distribution of BNS mergers. The posterior distribution of the hyperparameters in the model are to be inferred, using a GPU-accelerated MCMC algorithm. The intrinsic event rate density as a function of redshift is constrained up to z = 5, which a local rate density of R 0 = 22 . 6 − 10.6 + 15.0 Gpc − 3 yr − 1 , which is consistent with the latest gravitational-wave measurements.

Source: The Astrophysical Journal

DOI: 10.3847/1538-4357/aeaa3f

Authors: Tian-Yong Cao, Shu-Xu Yi, Yun-Fei Du, Emre S. Yorgancioglu, Shao-Lin Xiong

Institutions: Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing Academy of Science and Technology

Language: English

Topic: Gamma-ray bursts and supernovae

Subfield: Astronomy and Astrophysics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $4,499

Funders: National Natural Science Foundation of China, Chinese Academy of Sciences, Institute of High Energy Physics