Beyond Cuts in Small Signal Scenarios -- Enhanced Sneutrino Detectability Using Machine Learning

• URL: http://arxiv.org/abs/2108.03125v4
• Date: Fri, 7 Jul 2023 11:12:29 GMT
• Title: Beyond Cuts in Small Signal Scenarios -- Enhanced Sneutrino Detectability Using Machine Learning
• Authors: Daniel Alvestad, Nikolai Fomin, J\"orn Kersten, Steffen Maeland, Inga Str\"umke
• Abstract summary: We use two different models, XGBoost and a deep neural network, to exploit correlations between observables. We consider different methods to analyze the models' output, finding that a template fit generally performs better than a simple cut.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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