Beyond cuts in small signal scenarios

We investigate enhancing the sensitivity of new physics searches at the LHC by machine learning in the case of background dominance and a high degree of overlap between the observables for signal and background. We use two different models, XGBoost and a deep neural network, to exploit correlations...

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Vydané v:	The European physical journal. C, Particles and fields Ročník 83; číslo 5; s. 379 - 22
Hlavní autori:	Alvestad, Daniel, Fomin, Nikolai, Kersten, Jörn, Maeland, Steffen, Strümke, Inga
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Heidelberg Springer 01.05.2023 Springer Nature B.V SpringerOpen
Predmet:	Analysis Artificial neural networks Decision trees Kinematics Large Hadron Collider Machine learning Neural networks Physics Sensitivity enhancement
ISSN:	1434-6044, 1434-6052
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