Constraining the Higgs Potential with Neural Simulation-based Inference for Di-Higgs Production

TL;DR

This paper uses machine learning and simulation-based inference to enhance the sensitivity of di-Higgs production measurements, aiming to better constrain the Higgs potential and explore physics beyond the Standard Model at the LHC.

Contribution

It introduces a novel application of neural simulation-based inference to improve the analysis of di-Higgs events and constrain the Higgs potential more effectively.

Findings

Adding kinematic observables reduces degeneracies in Wilson coefficient likelihoods.

Simulation-based inference improves sensitivity to Higgs self-coupling.

Method enhances potential for discovering new physics at the LHC.

Abstract

Determining the form of the Higgs potential is one of the most exciting challenges of modern particle physics. Higgs pair production directly probes the Higgs self-coupling and should be observed in the near future at the High-Luminosity LHC. We explore how to improve the sensitivity to physics beyond the Standard Model through per-event kinematics for di-Higgs events. In particular, we employ machine learning through simulation-based inference to estimate per-event likelihood ratios and gauge potential sensitivity gains from including this kinematic information. In terms of the Standard Model Effective Field Theory, we find that adding a limited number of observables can help to remove degeneracies in Wilson coefficient likelihoods and significantly improve the experimental sensitivity.