Probing the Bias of Large-Scale Structure with Unlocalized Fast Radio Bursts

TL;DR

This paper develops a framework to measure the large-scale bias of unlocalized fast radio bursts (FRBs) as tracers of cosmic structure, accounting for localization uncertainties, and demonstrates its effectiveness on synthetic data.

Contribution

The authors introduce an end-to-end method to infer FRB bias from unlocalized data, incorporating smearing effects and likelihood inference, advancing LSS studies with FRBs.

Findings

The method accurately recovers true bias values from synthetic samples.

Localization uncertainties significantly suppress clustering signals but can be modeled.

Bias discrimination is more effective at lower redshifts.

Abstract

Large-scale structure (LSS) and tracer bias connect observable populations to the cosmic matter distribution. While galaxies are standard tracers, transient events such as gravitational-wave sources can also probe LSS despite large localization uncertainties. Fast radio bursts (FRBs), owing to their cosmological distances and dispersion-measure information, provide a promising complementary tracer of LSS. However, most FRBs lack precise localization and redshift measurements, introducing severe angular and radial errors that dilute the clustering signal. Here we construct an end-to-end framework to infer the linear large-scale bias of unlocalized FRB populations using the isotropic two-point correlation function. Our pipeline adopts the Landy-Szalay estimator with noise-matched random catalogs, a Monte Carlo forward model accounting for localization smearing, and likelihood-based inference with covariance matrices from lognormal mock samples. We test the method on synthetic FRB samples at redshifts z=0.3, 0.5, and 0.7 with injected bias values b=1.2, 1.5, and 2.0. The measured correlation functions closely follow smeared theoretical predictions, confirming that positional uncertainty dominates clustering suppression. Despite sample variance, the inferred bias posteriors recover the true inputs and preserve relative bias ordering. Discrimination is strongest at low redshift and weakens at higher redshift, where low-bias populations become poorly constrained. Our results demonstrate that meaningful large-scale clustering information can be extracted from poorly localized FRBs when smearing effects are properly modeled, establishing a practical route for future FRB-based LSS investigations.