Regular Article - Theoretical Physics
Beyond cuts in small signal scenarios
Enhanced sneutrino detectability using machine learning
Department of Mathematics and Physics, University of Stavanger, 4021, Stavanger, Norway
2 Department of Physics and Technology, University of Bergen, 5020, Bergen, Norway
3 Korea Institute for Advanced Study, 02455, Seoul, Republic of Korea
4 Western Norway University of Applied Sciences, 5063, Bergen, Norway
5 Department of Computer Science, Norwegian University of Science and Technology, 7034, Trondheim, Norway
Accepted: 18 April 2023
Published online: 8 May 2023
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 between observables and compare this approach to the traditional cut-and-count method. We consider different methods to analyze the models’ output, finding that a template fit generally performs better than a simple cut. By means of a Shapley decomposition, we gain additional insight into the relationship between event kinematics and the machine learning model output. We consider a supersymmetric scenario with a metastable sneutrino as a concrete example, but the methodology can be applied to a much wider class of models.
© The Author(s) 2023
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