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This research demonstrates how symbolic regression techniques can approximate computationally expensive LHC exclusion limits, enabling their real-time integration into global fits of Beyond the Standard Model theories. By converting complex experimental constraints into efficient mathematical expressions, the study bridges high-energy physics phenomenology with advanced machine learning methods to accelerate theoretical parameter scans. This approach represents a significant methodological advance in leveraging AI to solve long-standing computational bottlenecks in fundamental physics research.
Symbolic Classification-Enabled LHC Limits Online BSM Global Fits
arXiv:2605.22330v1 Announce Type: cross 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.
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.
High Energy Physics - Phenomenology (hep-ph); Machine Learning (cs.LG); Symbolic Computation (cs.SC); High Energy Physics - Experiment (hep-ex); High Energy Physics - Theory (hep-th)
arXiv:2605.22330 [hep-ph]
(or arXiv:2605.22330v1 [hep-ph] for this version)
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