Constraining the Higgs potential with neural simulation-based inference for di-Higgs production
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 beyo...
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| Hauptverfasser: | , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
3 September, 2024
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| In: |
Physical review
Year: 2024, Jahrgang: 110, Heft: 5, Pages: 1-20 |
| ISSN: | 2470-0029 |
| DOI: | 10.1103/PhysRevD.110.056004 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1103/PhysRevD.110.056004 Verlag, kostenfrei, Volltext: https://link.aps.org/doi/10.1103/PhysRevD.110.056004 
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| Verfasserangaben: | Radha Mastandrea, Benjamin Nachman, and Tilman Plehn |
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| 520 | |a 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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