Joint INFN-UNIMI-UNIMIB Pheno Seminars
Learning New Physics from a Machine
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Europe/Zurich
Edificio U2, Aula 5017 (Dipartimento di Fisica G. Occhialini, U2, Università degli Studi di Milano-Bicocca)
Edificio U2, Aula 5017
Dipartimento di Fisica G. Occhialini, U2, Università degli Studi di Milano-Bicocca
Piazza della Scienza, 3, 20126 Milano MI, Italia
Description
We propose using neural networks to detect data departures from a given reference model, with no prior bias on the nature of the new physics responsible for the discrepancy. The model-independent nature of our approach, and its ability to deal with rare signals such as those expected at the LHC, is quantitatively assessed in toy examples.