Multivariate approaches used in physics analyses by the High Energy Physics community often combine high-level observables estimated by very complex algorithms. The process to select these variables is usually based on a “brute force” approach, where all available event features are tested for multiple combinations of the algorithm hyperparameters. In this work, we propose an original method based on the use of a CancelOut layer to select to give as input to a Fully Connected Neural Network. Promising results are obtained in the development of a DNN classifier to select proton-proton collisions where a boosted Higgs boson decay to two b-quarks.
Tagging large-radius b-jets from Higgs decays dropping unneeded information / Di Luca, A.; Mascione, D.; Follega, F. M.; Cristoforetti, M.; Iuppa, R.. - In: POS PROCEEDINGS OF SCIENCE. - ISSN 1824-8039. - 380:(2022). (Intervento presentato al convegno PANIC 2021 tenutosi a Virtuale nel 5-10 Settembre 2021).
Tagging large-radius b-jets from Higgs decays dropping unneeded information
Di Luca A.;Mascione D.;Follega F. M.;Cristoforetti M.;Iuppa R.
2022-01-01
Abstract
Multivariate approaches used in physics analyses by the High Energy Physics community often combine high-level observables estimated by very complex algorithms. The process to select these variables is usually based on a “brute force” approach, where all available event features are tested for multiple combinations of the algorithm hyperparameters. In this work, we propose an original method based on the use of a CancelOut layer to select to give as input to a Fully Connected Neural Network. Promising results are obtained in the development of a DNN classifier to select proton-proton collisions where a boosted Higgs boson decay to two b-quarks.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione