Symbolic regression produces an approximate classifier for LHC exclusion limits that enables their direct inclusion during pMSSM global fits.
Interpreting LHC searches for new physics with SModelS
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abstract
ATLAS and CMS have performed a large number of searches for physics beyond the Standard Model (BSM). The results are typically presented in the context of Simplified Model Spectra (SMS), containing only a few new particles with fixed decay branching ratios, yielding generic upper limits on the cross section as a function of particle masses. The interpretation of these limits within realistic BSM scenarios is non-trivial and best done by automated computational tools. To this end we have developed SModelS, a public tool that can test any given BSM model with a $\mathbb{Z}_2$ symmetry by decomposing it into its SMS components and confronting them with a large database of SMS results. This allows to easily evaluate the main LHC constraints on the model. Additionally, SModelS returns information on important signatures that are not covered by the existing SMS results. This may be used to improve the coverage of BSM searches and SMS interpretations. We present the working principle of SModelS, in particular the decomposition procedure, the database and matching of applicable experimental results. Moreover, we present applications of SModelS to different models: the MSSM, a model with a sneutrino as the lightest supersymmetric particle and the UMSSM. These results illustrate how SModelS can be used to identify important constraints, untested regions and interesting new signatures.
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2026 1verdicts
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Symbolic Classification-Enabled LHC Limits Online BSM Global Fits
Symbolic regression produces an approximate classifier for LHC exclusion limits that enables their direct inclusion during pMSSM global fits.