Gravitational wave surrogates through automated machine learning

TL;DR

This paper explores using automated machine learning to efficiently generate accurate gravitational waveform surrogates from numerical relativity simulations, reducing manual effort and fine-tuning.

Contribution

It demonstrates that AutoML can produce high-accuracy gravitational waveform surrogates, including Gaussian process regression, without extensive manual intervention.

Findings

AutoML can generate surrogates nearly indistinguishable from NR simulations.

Gaussian process regression with radial basis kernels offers accurate, low-cost solutions.

AutoML framework is promising for gravitational waveform regression tasks.

Abstract

We analyze a prospect for predicting gravitational waveforms from compact binaries based on automated machine learning (AutoML) from around a hundred different possible regression models, without having to resort to tedious and manual case-by-case analyses and fine-tuning. The particular study of this article is within the context of the gravitational waves emitted by the collision of two spinless black holes in initial quasi-circular orbit. We find, for example, that approaches such as Gaussian process regression with radial bases as kernels do provide a sufficiently accurate solution, an approach which is generalizable to multiple dimensions with low computational evaluation cost. The results here presented suggest that AutoML might provide a framework for regression in the field of surrogates for gravitational waveforms. Our study is within the context of surrogates of numerical relativity simulations based on Reduced Basis and the Empirical Interpolation Method, where we find that for the particular case analyzed AutoML can produce surrogates which are essentially indistinguishable from the NR simulations themselves.