Deep Learning Models Resizing for High Energy Physics experiments

This thesis is focused on the development of novel approaches to improve the explainability of Deep Neural Network models in High Energy Physics to reduce the size of a certain model by dropping irrelevant input information. We show that it is possible to reduce the size of a signal-background classification problem by automatically ranking the relative importance of available particle jet input features. Variables are importance-sorted with a decision tree algorithm. The selected features can be used as input quantities for the classification problem at hand. A k-fold cross-validation is applied to raise the confidence in the extracted ranking. On the same line, a new Neural Network layer, called CancelOut, is presented as a tool to reduce the input parameter size by keeping the performance the highest during the training of the model. Both strategies are tested with the case of highly boosted di-jet resonances decaying to two b-quarks, to be selected against an overwhelming QCD background with a Deep Neural network. The data are produced via a pseudo experiment simulation.