Symbolic regression produces an approximate classifier for LHC exclusion limits that enables their direct inclusion during pMSSM global fits.
SModelS: a tool for interpreting simplified-model results from the LHC and its application to supersymmetry
3 Pith papers cite this work. Polarity classification is still indexing.
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abstract
We present a general procedure to decompose Beyond the Standard Model (BSM) collider signatures presenting a Z2 symmetry into Simplified Model Spectrum (SMS) topologies. Our method provides a way to cast BSM predictions for the LHC in a model independent framework, which can be directly confronted with the relevant experimental constraints. Our concrete implementation currently focusses on supersymmetry searches with missing energy, for which a large variety of SMS results from ATLAS and CMS are available. As show-case examples we apply our procedure to two scans of the minimal supersymmetric standard model. We discuss how the SMS limits constrain various particle masses and which regions of parameter space remain unchallenged by the current SMS interpretations of the LHC results.
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hep-ph 3representative citing papers
SModelS v3.0 largely reproduces ATLAS pMSSM electroweak-ino constraints, with CMS inclusion and combinations tightening limits but leaving some light ino parameter space viable.
citing papers explorer
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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.
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On the coverage of electroweak-inos within the pMSSM with SModelS -- a comparison with the ATLAS pMSSM study
SModelS v3.0 largely reproduces ATLAS pMSSM electroweak-ino constraints, with CMS inclusion and combinations tightening limits but leaving some light ino parameter space viable.
- Deciphering compressed electroweakino excesses with MadAnalysis 5