Efficient Gravitational Wave Template Bank Generation with Differentiable Waveforms

TL;DR

This paper introduces a fast, efficient method for generating gravitational wave template banks using automatic differentiation, enabling rapid and accurate coverage of parameter space for improved search pipelines.

Contribution

The authors develop a novel approach utilizing automatic differentiation to compute the parameter space metric, significantly speeding up template bank generation for gravitational wave searches.

Findings

Automatic differentiation accurately computes the metric for current waveforms.

Template banks can be rapidly generated using combined random placement and Monte Carlo methods.

The approach accelerates stochastic placement algorithms for gravitational wave template banks.

Abstract

The most sensitive search pipelines for gravitational waves from compact binary mergers use matched filters to extract signals from the noisy data stream coming from gravitational wave detectors. Matched-filter searches require banks of template waveforms covering the physical parameter space of the binary system. Unfortunately, template bank construction can be a time-consuming task. Here we present a new method for efficiently generating template banks that utilizes automatic differentiation to calculate the parameter space metric. Principally, we demonstrate that automatic differentiation enables accurate computation of the metric for waveforms currently used in search pipelines, whilst being computationally cheap. Additionally, by combining random template placement and a Monte Carlo method for evaluating the fraction of the parameter space that is currently covered, we show that search-ready template banks for frequency-domain waveforms can be rapidly generated. Finally, we argue that differentiable waveforms offer a pathway to accelerating stochastic placement algorithms. We implement all our methods into an easy-to-use Python package based on the jax framework, diffbank, to allow the community to easily take advantage of differentiable waveforms for future searches.