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

• Verfasst von: Mastandrea, Radha [VerfasserIn]
• Verfasst von: Nachman, Benjamin [VerfasserIn]
• Verfasst von: Plehn, Tilman [VerfasserIn]
• Titel: Constraining the Higgs potential with neural simulation-based inference for di-Higgs production
• Verf.angabe: Radha Mastandrea, Benjamin Nachman, and Tilman Plehn
• E-Jahr: 2024
• Jahr: 3 September, 2024
• Umfang: 20 S.
• Illustrationen: Illustrationen
• Fussnoten: Gesehen am 24.02.2025
• Titel Quelle: Enthalten in: Physical review
• Ort Quelle: Ridge, NY : American Physical Society, 2016
• Jahr Quelle: 2024
• Band/Heft Quelle: 110(2024), 5, Artikel-ID 056004, Seite 1-20
• ISSN Quelle: 2470-0029
• DOI: doi:10.1103/PhysRevD.110.056004
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1917884028

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.