Symbolic Classification-Enabled LHC Limits Online BSM Global Fits

TL;DR

This paper demonstrates a method to incorporate LHC limits into online BSM global fits efficiently using symbolic regression to derive classification expressions for the pMSSM parameter space.

Contribution

It introduces a novel approach leveraging symbolic regression to include LHC constraints in real-time global fits of BSM models.

Findings

Derived a mathematical expression classifying pMSSM allowed/excluded regions.

Successfully incorporated LHC Run-2 limits into a global fit.

Reduced computational cost of including LHC limits in global fits.

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.