Gravitational wave parameter estimation with compressed likelihood evaluations

TL;DR

This paper introduces a Reduced Order Quadratures (ROQ) method to significantly speed up gravitational wave parameter estimation by reducing the computational cost of likelihood evaluations, demonstrated on a four-dimensional GW burst model.

Contribution

The paper presents a novel ROQ-based technique that accelerates GW parameter estimation with minimal accuracy loss, applicable to complex GW models.

Findings

ROQ approach is 25 times faster than standard methods for the tested model.

ROQ maintains accuracy while significantly reducing computation time.

Potential to accelerate parameter estimation for more complex GW signals.

Abstract

One of the main bottlenecks in gravitational wave (GW) astronomy is the high cost of performing parameter estimation and GW searches on the fly. We propose a novel technique based on Reduced Order Quadratures (ROQs), an application and data-specific quadrature rule, to perform fast and accurate likelihood evaluations. These are the dominant cost in Markov chain Monte Carlo (MCMC) algorithms, which are widely employed in parameter estimation studies, and so ROQs offer a new way to accelerate GW parameter estimation. We illustrate our approach using a four dimensional GW burst model embedded in noise. We build an ROQ for this model, and perform four dimensional MCMC searches with both the standard and ROQs quadrature rules, showing that, for this model, the ROQ approach is around 25 times faster than the standard approach with essentially no loss of accuracy. The speed-up from using ROQs is expected to increase for more complex GW signal models and therefore has significant potential to accelerate parameter estimation of GW sources such as compact binary coalescences.