earching for New Physics is the primary goal of the CMS experiment at the LHC. Performing such search without relying on a specific theory extending the Standard Model (SM) is of paramount importance but at the same time highly non-trivial. Recently, several proposal have been made in that sense, in particular exploiting the power of modern ML techniques. The goal of this thesis is to apply such model-independent strategies to concrete physics cases, aiming at detecting signals solely by comparing the collision data with what predicted by the SM.

Development of a ML-based model-independent analysis strategy at the LHC

Ardino, Rocco
2019/2020

Abstract

earching for New Physics is the primary goal of the CMS experiment at the LHC. Performing such search without relying on a specific theory extending the Standard Model (SM) is of paramount importance but at the same time highly non-trivial. Recently, several proposal have been made in that sense, in particular exploiting the power of modern ML techniques. The goal of this thesis is to apply such model-independent strategies to concrete physics cases, aiming at detecting signals solely by comparing the collision data with what predicted by the SM.
2019-07-01
58
Particle Physics, Machine Learning, Deep Learning, Model-independent
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12608/23621