Accelerated gravitational-wave parameter estimation with reduced order modeling

TL;DR

This paper introduces a reduced order modeling approach that significantly accelerates gravitational-wave parameter estimation, making Bayesian inference more feasible for real-time analysis of binary neutron star inspirals.

Contribution

The paper demonstrates that reduced order quadrature within the LIGO Algorithm Library can speed up Bayesian inference by a factor of 30 to 150 for gravitational-wave data analysis.

Findings

Speed-up of 30 times for current detector configurations.

Potential to reduce analysis time from months to hours.

Applicable to other experiments requiring fast Bayesian inference.

Abstract

Inferring the astrophysical parameters of coalescing compact binaries is a key science goal of the upcoming advanced LIGO-Virgo gravitational-wave detector network and, more generally, gravitational-wave astronomy. However, current parameter estimation approaches for such scenarios can lead to computationally intractable problems in practice. Therefore there is a pressing need for new, fast and accurate Bayesian inference techniques. In this letter we demonstrate that a reduced order modeling approach enables rapid parameter estimation studies. By implementing a reduced order quadrature scheme within the LIGO Algorithm Library, we show that Bayesian inference on the 9-dimensional parameter space of non-spinning binary neutron star inspirals can be sped up by a factor of 30 for the early advanced detectors' configurations. This speed-up will increase to about $150$ as the detectors improve their low-frequency limit to 10Hz, reducing to hours analyses which would otherwise take months to complete. Although these results focus on gravitational detectors, the techniques are broadly applicable to any experiment where fast Bayesian analysis is desirable.