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, an approach which is generalizable to multiple dimensions with low computational evaluation cost, do provide a sufficiently accurate solution. 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.

中文翻译：

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通过自动机器学习替代引力波

我们分析了基于自动机器学习 (AutoML) 从大约一百种不同的可能回归模型中预测紧凑型双星引力波形的前景，而无需诉诸繁琐和手动的逐案分析和微调。这篇文章的具体研究是在初始准圆形轨道上两个无自旋黑洞碰撞所发出的引力波的背景下进行的。例如，我们发现，诸如以径向基为核的高斯过程回归之类的方法，一种可推广到多维且计算评估成本低的方法，确实提供了足够准确的解决方案。此处显示的结果表明，AutoML 可能会为引力波形替代领域的回归提供一个框架。