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...
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
3 September, 2024
|
| 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 
|
| Verfasserangaben: | Radha Mastandrea, Benjamin Nachman, and Tilman Plehn |
| Zusammenfassung: | 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. |
|---|---|
| Beschreibung: | Gesehen am 24.02.2025 |
| Beschreibung: | Online Resource |
| ISSN: | 2470-0029 |
| DOI: | 10.1103/PhysRevD.110.056004 |