Particle Swarm Optimization and gravitational wave data analysis: Performance on a binary inspiral testbed

TL;DR

This paper investigates the use of Particle Swarm Optimization (PSO) for detecting and estimating gravitational wave signals from binary inspirals, demonstrating its effectiveness in handling complex, multi-modal likelihood functions.

Contribution

It is the first to evaluate PSO in gravitational wave data analysis, showing its potential to reduce computational costs in complex signal searches.

Findings

PSO effectively handles highly multi-modal functions.

PSO reduces computational costs in GW signal detection.

PSO shows promise for further GW data analysis applications.

Abstract

The detection and estimation of gravitational wave (GW) signals belonging to a parameterized family of waveforms requires, in general, the numerical maximization of a data-dependent function of the signal parameters. Due to noise in the data, the function to be maximized is often highly multi-modal with numerous local maxima. Searching for the global maximum then becomes computationally expensive, which in turn can limit the scientific scope of the search. Stochastic optimization is one possible approach to reducing computational costs in such applications. We report results from a first investigation of the Particle Swarm Optimization (PSO) method in this context. The method is applied to a testbed motivated by the problem of detection and estimation of a binary inspiral signal. Our results show that PSO works well in the presence of high multi-modality, making it a viable candidate method for further applications in GW data analysis.