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  • Open Access

Data-Driven Discovery Strategy for Standard Model Effective Field Theory Searches

Martin Hirsch, Luca Mantani, and Veronica Sanz

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Phys. Rev. Lett. 135, 241801 – Published 9 December, 2025

DOI: https://doi.org/10.1103/rsc4-68tb

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 ultraviolet (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 large hadron collider and large electron-positron collider 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.

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Phys. Rev. Lett. 135, 241801– Published 9 December, 2025
  • Received 29 July 2025
  • Accepted 20 November 2025
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI. Funded by SCOAP3.

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