Validating Sequential Monte Carlo for Gravitational-Wave Inference

TL;DR

This paper validates the use of Sequential Monte Carlo, specifically persistent sampling, as an efficient alternative to nested sampling for gravitational-wave inference, demonstrating comparable results with improved efficiency and speed.

Contribution

It introduces and validates persistent sampling, a type of SMC, as a new viable method for gravitational-wave data analysis, showing it matches nested sampling in accuracy while being more efficient.

Findings

Persistent sampling produces results consistent with nested sampling.

Persistent sampling is approximately twice as efficient.

Persistent sampling is about 2.74 times faster.

Abstract

Nested sampling (NS) is the preferred stochastic sampling algorithm for gravitational-wave inference for compact binary coalenscences (CBCs). It can handle the complex nature of the gravitational-wave likelihood surface and provides an estimate of the Bayesian model evidence. However, there is another class of algorithms that meets the same requirements but has not been used for gravitational-wave analyses: Sequential Monte Carlo (SMC), an extension of importance sampling that maps samples from an initial density to a target density via a series of intermediate densities. In this work, we validate a type of SMC algorithm, called persistent sampling (PS), for gravitational-wave inference. We consider a range of different scenarios including binary black holes (BBHs) and binary neutron stars (BNSs) and real and simulated data and show that PS produces results that are consistent with NS whilst being, on average, 2 times more efficient and 2.74 times faster. This demonstrates that PS is a viable alternative to NS that should be considered for future gravitational-wave analyses.