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

Peter Athron, Csaba Balázs, Jon Butterworth, Christopher Chang, Andrew Fowlie, Tomás Gonzalo, Adil Jueid, Anders Kvellestad, Michele Lucente, Farvah Mahmoudi, Gregory D. Martinez, Are Raklev, Roberto Ruiz de Austri, Cristian Sierra, Wei Su, Aaron C. Vincent, Martin White, Lei Wu

TL;DR

This review traces the evolution of global fits in particle physics from early electroweak precision analyses to contemporary, data-rich explorations of beyond-the-Standard-Model scenarios. It emphasizes the shift from parameter estimation within constrained frameworks to comprehensive model comparison across SUSY, extended Higgs sectors, dark matter, neutrino physics, and EFTs, leveraging public tools such as GAMBIT. The article also discusses the challenges of high-dimensional parameter spaces, the need for public likelihoods, and the role of machine learning and AI in accelerating sampling, emulation, and theory-experiment comparisons. It highlights the precision frontier, bottom-up modeling with SMEFT/DMEFT, and future collider prospects as central to guiding discoveries, with ML offering substantial opportunities and caveats for the next generation of global fits.

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.

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

TL;DR

This review traces the evolution of global fits in particle physics from early electroweak precision analyses to contemporary, data-rich explorations of beyond-the-Standard-Model scenarios. It emphasizes the shift from parameter estimation within constrained frameworks to comprehensive model comparison across SUSY, extended Higgs sectors, dark matter, neutrino physics, and EFTs, leveraging public tools such as GAMBIT. The article also discusses the challenges of high-dimensional parameter spaces, the need for public likelihoods, and the role of machine learning and AI in accelerating sampling, emulation, and theory-experiment comparisons. It highlights the precision frontier, bottom-up modeling with SMEFT/DMEFT, and future collider prospects as central to guiding discoveries, with ML offering substantial opportunities and caveats for the next generation of global fits.

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.

Paper Structure

This paper contains 15 sections, 2 equations, 3 figures.

Table of Contents

  1. Introduction
  2. Past and present physics applications
  3. Electroweak precision tests
  4. Supersymmetric models
  5. Two Higgs doublet models
  6. Flavour
  7. Dark matter
  8. Neutrino physics
  9. Effective field theories
  10. Future applications, prospects and challenges
  11. The precision frontier
  12. Switching from top-down to bottom-up
  13. Testing as well as forecasting
  14. Impact of revolution in Machine Learning
  15. Summary

Figures (3)

  • Figure 1: Sketch of the elements of a global fit in particle physics --- through the fit (purple box), we combine a new physics theory and experimental searches, starting from the Lagrangian and going all the way to scientific conclusions. Red boxes show the key elements and green boxes show examples of computer programs that take them as inputs and deliver them as outputs.
  • Figure 2: Evolution of global fits of the CMSSM in ($m_0$, $m_{1/2}$). The fits use different methodologies and datasets.
  • Figure 3: ML could play a role in global fits by speeding up the computation of expensive observables inside a fit using a traditional sampling algorithm (left) e.g. event generation, or by driving the entire fit, replacing traditional sampling algorithms (right).