Signal to background discrimination for the production of double Higgs boson events via vector boson fusion mechanism in the decay channel with four charged leptons and two b-jets in the final state at the LHC experiment
| Autor: | BRUNELLA D'ANZI, Filippis, Nicola, Elmetenawee, Walaa, Miniello, Giorgia |
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| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | INSPIRE-HEP |
| ISSN: | 1742-6596 1742-6588 |
| DOI: | 10.1088/1742-6596/2438/1/012122 |
| Popis: | At the CERN Large Hadron Collider experiment, the non-resonant double Higgs production via vector-boson fusion represents a unique mean to probe the VVHH (V=Z, W$^{\pm}$) Higgs self-coupling at the current center of mass energies. Such a rare signal cannot be separated efficiently from huge backgrounds by applying a few-observables cut-based selection. Indeed, in this work, a Deep Learning algorithm is used to decide whether an event is more signal- or background-like. In particular, we report on two main aspects: results of a hyper-parameters parallel scanning strategy to distribute the training process across multiple nodes on the ReCaS-Bari data center computing resources and the discriminating performance of a Deep Neural Network architecture. 6 pages, 13 figures. Contribution to ACAT 2021: 20th International Workshop on Advanced Computing and Analysis Techniques in Physics Research, Daejeon, Kr, 29 Nov - 3 Dec 2021 |
| Databáze: | OpenAIRE |
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