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

TL;DR

This paper reviews the evolution and current role of global fits in searching for physics beyond the Standard Model, highlighting advances, challenges, and future prospects including machine learning integration.

Contribution

It provides a comprehensive overview of how global fits have expanded from minimal models to complex scenarios, emphasizing new methodologies and future directions.

Findings

Global fits have evolved to include diverse BSM models.

Shift from parameter estimation to model comparison.

Potential for AI to enhance fit efficiency and analysis.

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.