Learning to identify electrons

We investigate whether state-of-the-art classification features commonly used to distinguish electrons from jet backgrounds in collider experiments are overlooking valuable information. A deep convolutional neural network analysis of electromagnetic and hadronic calorimeter deposits is compared the performance typical features, revealing a $\ensuremath{\approx}5%$ gap which indicates that these lower-level data do contain untapped power. To reveal nature this unused information, we use recently developed technique map into space physically interpretable observables. identify two simple observables not typically for electron identification, but mimic decisions nearly close gap.