Metric Assisted Stochastic Sampling (MASS) search for gravitational waves from binary black hole mergers

TL;DR

This paper introduces MASS, a stochastic search algorithm for gravitational wave detection that offers comparable sensitivity to traditional methods but with simpler analysis and better scalability, demonstrated on LIGO data.

Contribution

The paper presents a new stochastic search algorithm for gravitational waves that simplifies analysis and improves scalability compared to traditional template bank methods.

Findings

Recovered eight known gravitational wave candidates from LIGO data.

Demonstrated sensitivity with simulated binary black hole signals.

Achieved competitive performance with standard pipelines.

Abstract

We present a novel gravitational wave detection algorithm that conducts a matched filter search stochastically across the compact binary parameter space rather than relying on a fixed bank of template waveforms. This technique is competitive with standard template-bank-driven pipelines in both computational cost and sensitivity. However, the complexity of the analysis is simpler allowing for easy configuration and horizontal scaling across heterogeneous grids of computers. To demonstrate the method we analyze approximately one month of public LIGO data from July 27 00:00 2017 UTC - Aug 25 22:00 2017 UTC and recover eight known confident gravitational wave candidates. We also inject simulated binary black hole (BBH) signals to demonstrate the sensitivity.