Constraints on fast radio burst population from the first CHIME/FRB catalog with the Hierarchical Bayesian Inference

TL;DR

This paper uses hierarchical Bayesian inference to analyze the redshift distribution of FRBs from the CHIME/FRB catalog, revealing a delay relative to star formation history, which offers insights into their origins.

Contribution

It introduces a hierarchical Bayesian method to study FRB redshift distributions, accounting for observational uncertainties and selection effects, providing new insights into FRB population characteristics.

Findings

FRB redshift distribution is delayed compared to star formation history

Hierarchical Bayesian approach effectively models FRB population

Results suggest possible links between FRBs and cosmic evolution

Abstract

Fast Radio Bursts (FRBs) have emerged as one of the most dynamic areas of research in astronomy and cosmology. Despite increasing number of FRBs have been reported, the exact origin of FRBs remains elusive. Investigating the intrinsic redshift distributions of FRBs could provide valuable insights into their possible origins and enhance the power of FRBs as a cosmological probe. In this paper, we propose a hierarchical Bayesian inference approach combining with several viable models to investigate the redshift distribution of the CHIME/FRB catalog 1. By utilizing this method, we aim to uncover the underlying patterns and characteristics of the FRB population, i.e. intrinsic redshift distribution of FRB. Taking uncertainties within the observational data and selection effects into consideration, we obtained that the redshift distribution of FRBs is significantly delayed with respect to that of the star formation history.