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  • Open Access

Symbolic classification-enabled LHC limits for BSM global fits

Shehu AbdusSalam*

  • *Contact author: abdussalam@sbu.ac.ir
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Phys. Rev. D 114, 055034 – Published 18 September, 2026

DOI: https://doi.org/10.1103/34q9-cb9w

Abstract

Global fits of beyond the Standard Model (BSM) physics often involve a two-way interplay between theory and experiment. Theoretical models provide guidance for experimental searches, while experimental results, in turn, constrain theoretical frameworks. A crucial aspect of this feedback loop is the direct inclusion of measurements and exclusion limits “online” global fits, i.e. during the parameter scans aspects of the global fits. However, incorporating the Large Hadron Collider (LHC) limits into such analyses has been computationally prohibitive, often due to time taken per parameter point exceeding the scales acceptable for global fit frameworks. In this study, we show that LHC limits can be incorporated online global fits by leveraging approximations derived from symbolic regression techniques. We utilize a dataset of ATLAS constraints from searches for electroweakino productions to derive a mathematical expression capable of classifying the phenomenological minimal supersymmetric Standard Model (pMSSM) parameter space as allowed or excluded. This is subsequently incorporated for making a global fit of the pMSSM to data, including the LHC Run-2 limits.

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Corrections

30 September, 2026

Correction: A footnote on page 1 was inadvertently included and has been removed.

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Phys. Rev. D 114, 055034– Published 18 September, 2026
  • Received 12 April 2026
  • Accepted 24 August 2026
  • Corrected 30 September 2026
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