Memory effect or cosmic string? Classifying gravitational-wave bursts with Bayesian inference

In the event of a gravitational-wave burst candidate, a key question will be which astrophysical signal hypothesis is most likely? Several different gravitational-wave transient sources can be modeled in the Fourier domain using a simple power law. This 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 memory and other power-law bursts to demonstrate model selection in support of signal detection and for use in parameter estimation. We show how Bayesian inference can be used to measure the power-law spectral index, thereby distinguishing between different astrophysical scenarios. We propose a strategy for model selection of power-law burst signals for gravitational-wave candidates, and we aim to use this analysis to determine whether a specific candidate can be best described by a compact binary coalescence (CBC) signal or by some other interesting astrophysical mechanism.