Time delay of fast radio burst population with respect to the star formation history
Hai-Nan Lin, Xin-Yi Li, Rui Zou

TL;DR
This study uses Bayesian analysis of CHIME/FRB data to determine the redshift distribution of FRBs, finding they lag star formation by billions of years and do not directly follow the star formation history.
Contribution
It introduces a Bayesian framework to analyze FRB populations considering various delay models, conclusively ruling out the star formation history as a direct tracer.
Findings
Log-normal delay model best fits the data.
FRBs lag star formation by 3-5 billion years.
Energy function parameters are tightly constrained.
Abstract
In spite of significant progress in the research of fast radio bursts (FRBs) in recent decade, their origin is still under extensive debate. Investigation on the population of FRBs can provide new insight into this interesting problem. In this paper, based on the first CHIME/FRB catalog, we construct a Bayesian framework to analyze the FRB population, with the selection effect of the CHIME telescope being properly taken into account. The energy function is modeled as the power-law with an exponential cutoff. Four redshift distribution models are considered, i.e., the star formation history (SFH) model, and three time-delayed models (Gaussian delay, log-normal delay, and power-law delay). The free parameters are simultaneously constrained using Bayesian inference method, and the Bayesian information criterion (BIC) is used in model comparison. According to BIC, the log-normal delay model…
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Taxonomy
TopicsPulsars and Gravitational Waves Research · Gamma-ray bursts and supernovae · Astronomy and Astrophysical Research
