Gravitational wave surrogates through automated machine learning

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.

arXiv comment15 pages, 7 figures

References in corpus (8)

• Scikit-learn: Machine Learning in Python
• XGBoost: A Scalable Tree Boosting System
• Fast prediction and evaluation of gravitational waveforms using surrogate models
• Fast and accurate prediction of numerical relativity waveforms from binary black hole coalescences using surrogate models
• Enhancing Gravitational-Wave Science with Machine Learning
• Reduced basis catalogs for gravitational wave templates
• Reduced Order and Surrogate Models for Gravitational Waves
• On ab initio-based, free and closed-form expressions for gravitational waves

Cited by in corpus (4)

• The Science of the Einstein Telescope
• Combining effective-one-body accuracy and reduced-order-quadrature speed for binary neutron star merger parameter estimation with machine learning
• A machine learning classifier for LOFAR radio galaxy cross-matching techniques
• Deep Residual Error and Bag-of-Tricks Learning for Gravitational Wave Surrogate Modeling