Accelerated Time-domain Analysis for Gravitational Wave Astronomy

TL;DR

This paper introduces a fully time-domain approach to gravitational-wave inference, leveraging hardware acceleration and structured linear algebra to improve speed and scalability over traditional frequency-domain methods.

Contribution

It develops a self-contained, end-to-end time-domain formulation and implements 'tdanalysis', enabling efficient likelihood evaluation with GPU support for gravitational wave data analysis.

Findings

Achieves significant speedups in likelihood evaluation.

Validates the method with injections and real data.

Supports gaps, boundaries, and multiple segments in data.

Abstract

Most current compact-binary searches and parameter-estimation pipelines evaluate the Gaussian-noise likelihood approximately using frequency-domain inner products with great success in analyzing gravitational-wave signals. This is historically motivated by (i) the approximate stationarity of detector noise on sufficiently long timescales, allowing a circulant approximation in the domain that diagonalizes the noise covariance in the Fourier basis, and (ii) the efficiency of matched filtering via fast Fourier transforms. However, the advantage of frequency-domain analysis comes with its own limitations. In this article, we develop a self-contained, end-to-end, \emph{fully time-domain} formulation of gravitational-wave inference and present an implementation that makes the likelihood evaluation practical at scale by exploiting structured linear algebra, software, and hardware acceleration. We validate the method using injections and demonstrate speedups for likelihood evaluation and on modern GPUs. We present \emph{tdanalysis}, an accelerated implementation that handles gaps, sharp boundaries, and multiple disjoint segments, and supports GPUs. We demonstrate some of its applications in gravitational wave astronomy.