Robust, Rapid, and Simple Gravitational-wave Parameter Estimation

TL;DR

This paper introduces a fast, robust, and simple method for gravitational-wave parameter estimation using metric-based importance sampling, enabling rapid analysis of complex sources with modest computational resources.

Contribution

The paper presents a novel importance sampling approach that significantly improves the speed and robustness of gravitational-wave parameter estimation, applicable to high-dimensional parameter spaces.

Findings

Achieves median 35% effective sampling efficiency for neutron star binaries.

Attains 20% efficiency for binary black holes with two observatories.

Reaches 9% efficiency for three-observatory networks.

Abstract

Rapid and robust parameter estimation of gravitational-wave sources is a key component of modern multi-messenger astronomy. We present a novel and straightforward method for rapid parameter estimation of gravitational-wave sources that uses metric-based importance sampling. The method enables robust parameter estimation of binary neutron star and binary black hole binaries and is trivially parallelized, enabling parameter estimation in seconds with modest resources. The algorithm achieves a median $35\%$ effective sampling efficiency for a population of aligned-spin neutron star binaries sources. Surprisingly, this approach is also highly efficient for analyzing the full 15-dimensional parameter space of typical binary black holes, with a population median $20\%$ efficiency achieved for a source detected primarily by the twin LIGO observatories and $9\%$ for a network of three comparable sensitivity observatories. This method can serve immediate use to improve the low-latency data products of the gravitational-wave observatory network and may be a key component of how the millions of sources observed by next-generation observatories could be analyzed. The approach can also be broadly applied for problems where an approximate likelihood metric-space can be constructed.