Skip to main navigation Skip to search Skip to main content

Global fits and the search for new physics: past, present and future

  • Nanjing Normal University
  • Nanjing Key Laboratory of Particle Physics and Astrophysics
  • Monash University
  • University College London
  • University of Oslo
  • X-HEP Laboratory
  • Karlsruhe Institute of Technology
  • Institute for Basic Science
  • Chung-Ang University
  • University of Bologna
  • National Institute for Nuclear Physics
  • Universite Claude Bernard Lyon 1
  • CERN
  • Institut universitaire de France
  • University of California at Los Angeles
  • University of Valencia
  • Shanghai Jiao Tong University
  • Sun Yat-Sen University
  • Queen's University Kingston
  • University of Adelaide

Research output: Contribution to journalReview articlepeer-review

Abstract

In this work, we review the history and current role of global fits in the search for physics beyond the Standard Model (BSM), including precision tests of the Standard Model (SM). Although BSM global fits were initially focused on minimal supersymmetric models, we describe how fits have evolved in response to new data from the Large Hadron Collider (LHC) and elsewhere, expanding to encompass a broad spectrum of BSM scenarios including non-minimal supersymmetry, axion-like particles, extended Higgs sectors, dark matter models, and effective field theories such as SMEFT. We discuss how the role of global fits has shifted from forecasting possible signals of new physics at the LHC to understanding the impact of null results from LHC run-I and II and the discovery of the Higgs boson, and how interest has shifted from global fits for parameter estimation to comprehensive model comparison. We close by discussing potential trends and future applications, emphasizing the potential for machine learning and artificial intelligence to enhance the efficiency of sampling algorithms and comparison between theory and experiment, as well as collaboration and software development.

Original languageEnglish
JournalScience Bulletin
DOIs
Publication statusAccepted/In press - 2026

Keywords

  • Global fits
  • LHC
  • Machine learning
  • New physics beyond the SM

Cite this