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Physicists Learning from Machines Learning

Taylor Faucett, University of California, Irvine
Thursday, November 19, 2020 - 12:30pm
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Machine Learning methods are extremely powerful but often function as black-box problem solvers, providing improved performance at the expense of clarity. Our work describes a new machine learning approach, which translates the strategy of a deep neural network into simple functions that are meaningful and intelligible to the physicist, without sacrificing performance improvements. We apply this approach to benchmark high-energy problems of fat-jet classification and electron identification. In each case, we find simple new observables that provide additional classification power and novel insights
into the nature of the problem.