Taylor Faucett, University of California, Irvine
Zoom (https://washington.zoom.us/j/91439926016)
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.