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Constraining the Higgs potential with neural simulation-based inference for di-Higgs production

Mastandrea, Radha·Nachman, Benjamin·Plehn, Tilman
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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.

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72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDSBayesian methodsartificial neural networkseffective field theoryextensions of Higgs sectormachine learningsignatures with Higgs bosons

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Mastandrea, Radha, Nachman, Benjamin, Plehn, Tilman. 2024-09-03. Constraining the Higgs potential with neural simulation-based inference for di-Higgs production. https://doi.org/10.1103/physrevd.110.056004

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