Symbolic Classification-Enabled LHC Limits Online BSM Global Fits

2026-05-21

AI Summary

研究通过符号回归技术生成的近似方法，将LHC实验限制直接纳入BSM物理模型（如pMSSM）的全局拟合中，解决了计算成本高的问题。

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.

Problem

• BSM
• global fits
• LHC limits
• parameter scans
• symbolic regression

Innovation

• symbolic regression
• LHC limits
• global fits
• pMSSM