Data-driven discovery strategy for standard model effective field theory searches

TL;DR

This paper introduces a genetic algorithm-based method to enhance the detection of new physics signals in collider data within the SMEFT framework, outperforming traditional analyses.

Contribution

It develops a novel, scalable approach using genetic algorithms for systematic SMEFT operator subset selection without prior UV assumptions.

Findings

Successfully recovers relevant operator subsets in collider data

Improves sensitivity to deviations over traditional methods

Validated on current and projected high-luminosity data

Abstract

We present a novel strategy to uncover indirect signs of new physics in collider data using the Standard Model Effective Field Theory (SMEFT) framework, offering notably improved sensitivity compared to traditional global analyses. Our approach leverages genetic algorithms to efficiently navigate the high-dimensional space of operator subsets, identifying deformations that improve agreement with data without relying on prior UV assumptions. This enables the systematic detection of SMEFT scenarios that outperform the Standard Model in explaining observed deviations. We validate the approach on current LHC and LEP measurements, perform closure tests with injected UV signals, and assess performance under high-luminosity projections. The algorithm successfully recovers relevant operator subsets and highlights directions in parameter space where deviations are most likely to emerge. Our results demonstrate the potential of SMEFT-based discovery searches driven by model selection, providing a scalable framework for future data analyses.