Improving the scalability of Gaussian-process error marginalization in gravitational-wave inference

TL;DR

This paper enhances Gaussian process error marginalization in gravitational-wave inference by introducing GPU acceleration, improved training set construction, and a noise-adapted likelihood, making the method more scalable and practical for complex data.

Contribution

It presents novel improvements to Gaussian process error marginalization, addressing scalability issues in gravitational-wave inference with higher-dimensional data.

Findings

GPU-accelerated training significantly reduces computation time

Improved training set construction enhances model accuracy

New likelihood better handles detector noise

Abstract

The accuracy of Bayesian inference can be negatively affected by the use of inaccurate forward models. In the case of gravitational-wave inference, accurate but computationally expensive waveform models are sometimes substituted with faster but approximate ones. The model error introduced by this substitution can be mitigated in various ways, one of which is by interpolating and marginalizing over the error using Gaussian process regression. However, the use of Gaussian process regression is limited by the curse of dimensionality, which makes it less effective for analyzing higher-dimensional parameter spaces and longer signal durations. In this work, to address this limitation, we focus on gravitational-wave signals from extreme-mass-ratio inspirals as an example, and propose several significant improvements to the base method: an improved prescription for constructing the training set, GPU-accelerated training algorithms, and a new likelihood that better adapts the base method to the presence of detector noise. Our results suggest that the new method is more viable for the analysis of realistic gravitational-wave data.