From Optimal Observables to Machine Learning: an Effective-Field-Theory Analysis of $e^+e^- \to W^+W^-$ at Future Lepton Colliders

TL;DR

This paper demonstrates that machine-learning techniques, especially simulation-based inference, outperform traditional optimal observables in effective-field-theory analysis of $e^+e^- o W^+W^-$ at future lepton colliders, offering more robust and unbiased results.

Contribution

It introduces a machine-learning framework for EFT analysis of $e^+e^- o W^+W^-$, showing advantages over conventional methods and applicability to other collider processes.

Findings

Machine-learning methods are more robust to detector effects and backgrounds.

Simulation-based inference can produce unbiased results with sufficient data.

Framework applicable to other EFT analyses at future colliders.

Abstract

We apply machine-learning techniques to the effective-field-theory analysis of the $e^+e^- \to W^+W^-$ processes at future lepton colliders, and demonstrate their advantages in comparison with conventional methods, such as optimal observables. Compared to traditional algorithms, we show that simulation-based inference methods are more robust to detector effects and backgrounds, and could in principle produce unbiased results with sufficient Monte Carlo simulation samples that accurately describe experiments. This is crucial for the analyses at future lepton colliders given the outstanding precision of the $e^+e^- \to W^+W^-$ measurement ($\sim 10^{-4}$ in terms of anomalous triple gauge couplings or even better) that can be reached. Our framework can be generalized to other effective-field-theory analyses, such as the one of $e^+e^- \to t\bar{t}$ or similar processes at muon colliders.