PoS - Proceedings of Science
Volume 414 - 41st International Conference on High Energy physics (ICHEP2022) - Computing and Data Handling
Imposing exclusion limits on new physics with machine-learned likelihoods
E. Arganda, M. de los Rios, A.D. Perez and R.M. Sandá Seoane*
Full text: pdf
Pre-published on: October 21, 2022
Published on: June 15, 2023
Machine-Learned Likelihood (MLL) is a method that, by combining modern machine-learning techniques with likelihood-based inference tests, allows estimating the experimental sensitivity of high-dimensional data sets. Here we extend the MLL method by including the exclusion hypothesis tests and study it first on a toy model of multivariate Gaussian distributions, where the true probability distribution functions are known. We then apply it to a case of interest in the search for new physics at the LHC, in which a $Z^\prime$ boson decays into lepton pairs, comparing the performance of MLL for estimating 95\% CL exclusion limits with respect to the prospects reported by ATLAS at 14 TeV with a luminosity of 3 ab$^{-1}$.
DOI: https://doi.org/10.22323/1.414.1226
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