Beyond cuts in small signal scenarios: Enhanced sneutrino detectability using machine learning
Peer reviewed, Journal article
Published version
Permanent lenke
https://hdl.handle.net/11250/3073136Utgivelsesdato
2023Metadata
Vis full innførselSamlinger
Originalversjon
Alvestad, D., Fomin, N., Kersten, J.,Mæland, S. & Strumke, I. (2023) Beyond cuts in small signal scenarios. Eur. Phys. J. C 83, 379. 10.1140/epjc/s10052-023-11532-9Sammendrag
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.