The dispersion measure and scattering of Fast Radio Bursts: contributions from multi-components, and clues for the intrinsic properties

Jian-Feng Mo, Weishan Zhu, Long-Long Feng

Published 2025-02-09Version 1

Fast radio bursts (FRBs) are luminous, millisecond-duration transients that offer great potential for probing the universe, yet their physical origins remain unclear. The dispersion measure (DM) and scattering time ($\tau$) distributions provide key insights into FRBs' properties, including source population, redshift, and energy distribution. We use a simplified model of FRB source population and intrinsic Schechter function-like energy distribution, coupled with a thorough assessment of various contributors to dispersion and scattering, to replicate the joint distribution of DM and $\tau$ in the CHIME/FRB catalog. A mixed FRB source population, including both young and old progenitors, is considered. Contributions to the DM and $\tau$ from interstellar medium (ISM), circumgalactic medium (CGM) within host and foreground halos are informed by the IllustrisTNG simulation, while contributions from the Milky Way, intergalactic medium (IGM), and local environmental are estimated by updated models. Using MCMC simulations, we identify optimal model that well reproduce the DM distribution and broadly reproduce the $\tau$ distribution in the CHIME/FRB catalog. Our model suggests that the fraction of FRBs tracing star-formation rate is $\rm{f_{PSFR}=0.58^{+0.16}_{-0.27}}$, while $\rm{log_{10}E_*[erg]=42.27^{+1.17}_{-1.18}}$ and $\gamma=-1.60^{+0.11}_{-0.13}$ in the energy distribution function. Scattering predominantly arises from the circumburst medium or the ISM and CGM of hosts, which cause a DM of $\sim 10\, \rm{pc\,cm^{-3}}$. Using our optimal model, we estimate FRB redshifts with two methods: DM-only and combined DM-$\tau$. Evaluation with 68 localized FRBs reveals an RMS error $0.11-0.12$, and incorporation of $\tau$ has a minor effect. We further argue that the host galaxy properties of localized FRBs could be a potential tool to validate our model in the future.