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Report on 2404.07822v1

Article scientifique 2024 Anglais

Résumé

To mitigate the model dependencies of searches for new narrow resonances at the Large Hadron Collider (LHC), semi-supervised Neural Networks (NNs) can be used.Unlike fully supervised classifiers these models introduce an additional look-elsewhere effect in the process of optimising thresholds on the response distribution.We perform a frequentist study to quantify this effect, in the form of a trials factor.As an example, we consider simulated Zγ data to perform narrow resonance searches using semi-supervised NN classifiers.The results from this analysis provide substantiation that the look-elsewhere effect induced by the semi-supervised NN is under control.

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Lieberman, B., Crivellin, A., Dahbi, S., Stevenson, F., Tripathi, N., Kumar, M., Mellado, B. (2024). Report on 2404.07822v1. https://doi.org/10.21468/scipost.report.9128

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Auteur(s)

Benjamin Lieberman Andreas Crivellin Salah-Eddine Dahbi Finn Stevenson Nidhi Tripathi Mukesh Kumar Bruce Mellado

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