Abstract:
In this thesis, we examine to what extend machine learning techniques like convolutional neu-
ral networks can differentiate real and fake electrons better than the observables constructed
by physicists. Our approach is treating the electron as an image, with pixel intensities given
by local calorimeter deposits. Overall, the convolutional neural network outperforms the
traditional physics observable used, for most signal efficiencies. We also find that the per-
formance of the trained model was independent of the source of real and fake electrons. The
performance of the model matched our expectations while being tested on electrons from
different sources even those which the model wasn’t trained for. This suggests that the
network can extract relevant physical information about the real and fake electrons which
the traditional observables cannot. This classifier is also more robust than the data-driven
approaches used for fake electron estimation which relies on the source of electrons.
