Artificial proto-modelling with simplified-model results from the LHC
Abstract
We present a novel approach to identify potential dispersed signals of new physics in the slew of published LHC results. It employs a random walk algorithm to introduce sets of new particles, dubbed "proto-models", which are tested against simplified-model results from ATLAS and CMS searches for new physics by exploiting the SModelS software framework. A combinatorial algorithm identifies the set of analyses and/or signal regions that maximally violates the Standard Model hypothesis, while remaining compatible with the entirety of LHC constraints in our database. Crucial to the method is the ability to construct a reliable likelihood in proto-model space; we explain the various approximations which are needed depending on the information available from the experiments, and how they impact the whole procedure.
Cite
@article{arxiv.2105.09020,
title = {Artificial proto-modelling with simplified-model results from the LHC},
author = {Sabine Kraml and Andre Lessa and Wolfgang Waltenberger},
journal= {arXiv preprint arXiv:2105.09020},
year = {2021}
}
Comments
6 pages; contribution to the 2021 QCD session of the 55th Rencontres de Moriond