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

Mastandrea, Radha; Nachman, Benjamin; Plehn, Tilman

doi:10.1103/physrevd.110.056004

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

Journal Article · 02 September 2024 · Physical Review. D.

DOI: https://doi.org/10.1103/physrevd.110.056004 · OSTI ID:2439499

^[1];

^[2]; Plehn, Tilman ^[3]

University of California, Berkeley, CA (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); University of California, Berkeley, CA (United States)
Heidelberg Univ. (Germany)

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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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE; USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF); USDOE Office of Science (SC), High Energy Physics (HEP)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 2439499

Journal Information:: Physical Review. D., Journal Name: Physical Review. D. Journal Issue: 5 Vol. 110; ISSN 2470-0010

Publisher:: American Physical Society (APS)Copyright Statement

Country of Publication:: United States

Language:: English

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72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS
Bayesian methods
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Title: Constraining the Higgs potential with neural simulation-based inference for di-Higgs production

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