Orateur
Taylor Faucett
(Université Clermont Auvergne)
Description
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 find simple new jet substructure observables which provide improved classification power and novel insights into the nature of the problem.
Auteurs principaux
Taylor Faucett
(Université Clermont Auvergne)
Jesse Thaler
(Massachusetts Institute of Technology)
Daniel Whiteson
(University of California, Irvine)
