TY - JOUR
T1 - Global fits and the search for new physics
T2 - past, present and future
AU - The GAMBIT Collaboration:
AU - Athron, Peter
AU - Balázs, Csaba
AU - Butterworth, Jon
AU - Chang, Christopher
AU - Fowlie, Andrew
AU - Gonzalo, Tomás
AU - Jueid, Adil
AU - Kvellestad, Anders
AU - Lucente, Michele
AU - Mahmoudi, Farvah
AU - Martinez, Gregory D.
AU - Raklev, Are
AU - Ruiz de Austri, Roberto
AU - Sierra, Cristian
AU - Su, Wei
AU - Vincent, Aaron C.
AU - White, Martin
AU - Wu, Lei
N1 - Publisher Copyright:
© 2026 Science China Press
PY - 2026
Y1 - 2026
N2 - 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.
AB - 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.
KW - Global fits
KW - LHC
KW - Machine learning
KW - New physics beyond the SM
UR - https://www.scopus.com/pages/publications/105043072615
U2 - 10.1016/j.scib.2026.06.028
DO - 10.1016/j.scib.2026.06.028
M3 - Review article
AN - SCOPUS:105043072615
SN - 2095-9273
JO - Science Bulletin
JF - Science Bulletin
ER -