SciPost Physics Core (Nov 2024)

Trials factor for semi-supervised NN classifiers in searches for narrow resonances at the LHC

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

DOI
https://doi.org/10.21468/SciPostPhysCore.7.4.073
Journal volume & issue
Vol. 7, no. 4
p. 073

Abstract

Read online

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 $Z\gamma$ 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.