Memory Effect or Cosmic String? Classifying Gravitational-Wave Bursts with Bayesian Inference

TL;DR

This paper demonstrates how Bayesian inference can classify gravitational-wave bursts from different sources, such as cosmic strings and memory effects, by analyzing their spectral indices in simulated interferometer data.

Contribution

The study introduces a Bayesian framework for distinguishing gravitational-wave burst sources based on their spectral properties, enhancing source identification accuracy.

Findings

Bayesian inference accurately estimates spectral indices.

The method effectively differentiates between cosmic string and memory burst signals.

Simulation results validate the approach for gravitational-wave data analysis.

Abstract

A variety of gravitational-wave transient sources can be modeled in the Fourier domain using a power law. This simple power-law model provides a reasonable approximation for gravitational-wave bursts from cosmic string cusps, cosmic string kinks, and the memory effect. Each of these sources is described using a different spectral index. In this work, we simulate interferometer strain data with injections of power-law and memory bursts to demonstrate parameter estimation, signal detection, and model selection. We show how Bayesian inference can be used to measure the power-law spectral index, thereby distinguishing between different astrophysical scenarios.