Efficient Gravitational Wave Template Bank Generation with Differentiable Waveforms

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.

15 pages, 6 figures. Comments welcome! Code can be found at https://github.com/adam-coogan/diffbank. Updated to match PRD version

References in corpus (11)

• Array Programming with NumPy
• Multipolar Effective-One-Body Waveforms for Precessing Binary Black Holes: Construction and Validation
• A template bank to search for gravitational waves from inspiralling compact binaries I: physical models
• Gravitational waves from inspiralling compact binaries: hexagonal template placement and its efficiency in detecting physical signals
• Template-based searches for gravitational waves: efficient lattice covering of flat parameter spaces
• Template Bank for Compact Binary Coalescence Searches in Gravitational Wave Data: A General Geometric Placement Algorithm
• Impact of precession on aligned-spin searches for neutron-star--black-hole binaries
• PyLightcurve-torch: a transit modelling package for deep learning applications in PyTorch
• Kernel Phase and Coronagraphy with Automatic Differentiation
• Efficient generation and optimization of stochastic template banks by a neighboring cell algorithm
• Metric Assisted Stochastic Sampling (MASS) search for gravitational waves from binary black hole mergers

Cited by in corpus (7)

• Mock data study for next-generation ground-based detectors: The performance loss of matched filtering due to correlated confusion noise
• Combining effective-one-body accuracy and reduced-order-quadrature speed for binary neutron star merger parameter estimation with machine learning
• Gravitational waves and neutrino oscillations in Chern-Simons axion gravity
• A geometric template bank for the detection of spinning low-mass compact binaries with moderate orbital eccentricity
• Efficient Stochastic Template Bank using Inner Product Inequalities
• Analytic auto-differentiable $\Lambda$CDM cosmography
• One-stop strategy to search for long-duration gravitational-wave signals