Rapid and Robust Parameter Inference for Binary Mergers

TL;DR

This paper introduces a fast, robust Bayesian inference method for binary merger signals in gravitational wave data, effectively handling noise glitches and enabling rapid sky localization and parameter estimation.

Contribution

The paper presents a novel analysis technique combining wavelet de-noising, penalized likelihood maximization, and heterodyned likelihoods for glitch-resistant, rapid Bayesian inference of binary merger parameters.

Findings

Method produces Bayesian parameter estimates within minutes.

Robust against noise transients and glitches.

Provides accurate sky maps and mass estimates.

Abstract

The detection rate for compact binary mergers has grown as the sensitivity of the global network of ground based gravitational wave detectors has improved, now reaching the stage where robust automation of the analyses is essential. Automated low-latency algorithms have been developed that send out alerts when candidate signals are detected. The alerts include sky maps to facilitate electromagnetic follow up observations, along with probabilities that the system might contain a neutron star, and hence be more likely to generate an electromagnetic counterpart. Data quality issues, such as loud noise transients (glitches), can adversely affect the low-latency algorithms, causing false alarms and throwing off parameter estimation. Here a new analysis method is presented that is robust against glitches, and capable of producing fully Bayesian parameter inference, including sky maps and mass estimates, in a matter of minutes. Key elements of the method are wavelet-based de-noising, penalized maximization of the likelihood during the initial search, rapid sky localization using pre-computed inner products, and heterodyned likelihoods for full Bayesian inference.