Generating optimal Gravitational-Wave template banks with metric-preserving autoencoders

TL;DR

This paper introduces a metric-preserving autoencoder approach for creating efficient gravitational-wave template banks, reducing dimensionality more effectively than traditional linear methods like SVD, with broad applications in physics.

Contribution

The authors develop a non-linear dimensionality reduction method using metric-preserving autoencoders for gravitational wave template bank construction, outperforming SVD in requiring fewer dimensions.

Findings

Fewer dimensions needed for template banks using autoencoders compared to SVD.

Autoencoder-based banks maintain metric properties enabling uniform grid placement.

Method applicable to other physics domains like waveform modeling and parameter estimation.

Abstract

Matched filtering for signal detection in noisy data requires template banks that capture variation in signal waveforms while minimizing computational cost. Dimensionality reduction of signal waveforms can be important for building efficient template banks. In various domains of physics, dimensionality reduction is very commonly performed using linear methods such as singular value decomposition (SVD). This can, however, introduce redundancies if the signals span curved, nonlinear manifolds in parameter space. Alternatively, autoencoders are a type of neural networks that can be used for non-linear dimensionality reduction. We use a variation of the autoencoder which preserves the metric in its latent space ($g_{ij}^{\text{latent}} \approx g_{ij}^{\text{physical}}$); this enables template banks to be constructed by simply placing a uniform grid in the autoencoder's low-dimensional latent space. We apply our method for creating geometric template banks for gravitational wave searches and show that our banks require fewer dimensions compared to using the SVD basis. Our method can also be useful for other applications requiring dimensionality reduction, such as gravitational waveform modeling, fast parameter estimation and model-independent tests of general relativity. Finally, we discuss extensions to other domains including cosmological parameter estimation, and we show tests of our method in extreme cases of periodic signal manifolds.