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.

Deep Learning Models Resizing for High Energy Physics experiments / Di Luca, Andrea. - (2022 Apr 14), pp. 1-141. [10.15168/11572_338814]

Deep Learning Models Resizing for High Energy Physics experiments

Di Luca, Andrea
2022-04-14

Abstract

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.
14-apr-2022
XXXIV
2020-2021
Fisica (29/10/12-)
Physics
Iuppa, Roberto
Cristoforetti, Marco
no
Inglese
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/338814
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