RNLE: Residual neural likelihood estimation and its application to gravitational-wave astronomy

TL;DR

RNLE introduces a neural likelihood estimation method tailored for gravitational-wave data, efficiently modeling non-Gaussian noise to enhance parameter inference robustness and reduce simulation needs.

Contribution

It presents RNLE, a novel neural likelihood estimation approach that explicitly models noise in gravitational-wave signals, improving accuracy and efficiency over existing methods.

Findings

RNLE accurately models non-Gaussian noise in gravitational-wave data.

The method reduces the number of simulations needed for reliable inference.

RNLE remains robust in the presence of noise transients and glitches.

Abstract

Simulation-based inference provides a powerful framework for Bayesian inference when the likelihood is analytically intractable or computationally prohibitive. By leveraging machine-learning techniques and neural density estimators, it enables flexible likelihood or posterior modeling directly from simulations. We introduce Residual Neural Likelihood Estimation (RNLE), a modification of Neural Likelihood Estimation (NLE) that learns the likelihood of non-Gaussian noise in gravitational-wave detector data. Exploiting the additive structure of the signal and noise generation processes, RNLE directly models the noise distribution, substantially reducing the number of simulations required for accurate parameter estimation and improving robustness to realistic noise artifacts. The performance of RNLE is demonstrated using a toy model, simulated gravitational-wave signals, and real detector noise from ground based interferometers. Even in the presence of loud non-Gaussian transients, glitches, we show that RNLE can achieve reliable parameter recovery when trained on appropriately constructed datasets. We further assess the stability of the method by quantifying the variability introduced by retraining the conditional density estimator on statistically identical datasets with different optimization seeds, referred to as training noise. This variability can be mitigated through an ensemble approach that combines multiple RNLE models using evidence-based weighting. An implementation of RNLE is publicly available in the sbilby package, enabling its deployment within gravitational-wave astronomy and a broad range of scientific applications requiring flexible, simulation-based likelihood estimation.