In this talk I will demonstrate the potential of the black hole mass gap to probe new physics. The mass gap, in which no black holes can be formed, is a standard prediction of stellar structure theory. I will show that new physics that couples to the Standard Model can act as an additional source of energy loss in the cores of population-III stars, dramatically altering their evolution, resulting in large shifts of the gap. The gravitational wave observations by the LIGO/Virgo collaboration will bring the edges of the black hole mass gap in sight in the coming years, making this a promising novel probe of new physics.

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]]>We propose a theoretical understanding of neural networks in terms of Wilsonian effective field theory. The correspondence relies on the fact that many asymptotic neural networks are drawn from Gaussian processes, the analog of non-interacting field theories. Moving away from the asymptotic limit yields a non-Gaussian process and corresponds to turning on particle interactions, allowing for the computation of correlation functions of neural network outputs with Feynman diagrams. Minimal non-Gaussian process likelihoods are determined by the most relevant non-Gaussian terms, according to the flow in their coefficients induced by the Wilsonian renormalization group. This yields a direct connection between overparameterization and simplicity of neural network likelihoods. Whether the coefficients are constants or functions may be understood in terms of GP limit symmetries, as expected from 't Hooft's technical naturalness. General theoretical calculations are matched to neural network experiments in the simplest class of models allowing the correspondence. Our formalism is valid for any of the many architectures that becomes a GP in an asymptotic limit, a property preserved under certain types of training.

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