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• W3187581109 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 supersymmetric models." @default.
• W3187581109 created "2021-08-16" @default.
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• W3187581109 date "2021-08-09" @default.
• W3187581109 modified "2023-09-27" @default.
• W3187581109 title "Beyond Cuts in Small Signal Scenarios - Enhanced Sneutrino Detectability Using Machine Learning" @default.
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