Neural simulation-based inference of the Higgs trilinear self-coupling via off-shell Higgs production
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866911688745287680 |
|---|---|
| author | Ghosh, Aishik Griese, Maximilian Haisch, Ulrich Park, Tae Hyoun |
| author_facet | Ghosh, Aishik Griese, Maximilian Haisch, Ulrich Park, Tae Hyoun |
| contents | One of the forthcoming major challenges in particle physics is the experimental determination of the Higgs trilinear self-coupling. While efforts have largely focused on on-shell double- and single-Higgs production in proton-proton collisions, off-shell Higgs production has also been proposed as a valuable complementary probe. In this article, we design a hybrid neural simulation-based inference (NSBI) approach to construct a likelihood of the Higgs signal incorporating modifications from the Standard Model effective field theory (SMEFT), relevant background processes, and quantum interference effects. It leverages the training efficiency of matrix-element-enhanced techniques, which are vital for robust SMEFT applications, while also incorporating the practical advantages of classification-based methods for effective background estimates. We demonstrate that our NSBI approach achieves sensitivity close to the theoretical optimum and provide expected constraints for the high-luminosity upgrade of the Large Hadron Collider. While we primarily concentrate on the Higgs trilinear self-coupling, we also consider constraints on other SMEFT operators that affect off-shell Higgs production. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_02032 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Neural simulation-based inference of the Higgs trilinear self-coupling via off-shell Higgs production Ghosh, Aishik Griese, Maximilian Haisch, Ulrich Park, Tae Hyoun High Energy Physics - Phenomenology High Energy Physics - Experiment Data Analysis, Statistics and Probability Machine Learning One of the forthcoming major challenges in particle physics is the experimental determination of the Higgs trilinear self-coupling. While efforts have largely focused on on-shell double- and single-Higgs production in proton-proton collisions, off-shell Higgs production has also been proposed as a valuable complementary probe. In this article, we design a hybrid neural simulation-based inference (NSBI) approach to construct a likelihood of the Higgs signal incorporating modifications from the Standard Model effective field theory (SMEFT), relevant background processes, and quantum interference effects. It leverages the training efficiency of matrix-element-enhanced techniques, which are vital for robust SMEFT applications, while also incorporating the practical advantages of classification-based methods for effective background estimates. We demonstrate that our NSBI approach achieves sensitivity close to the theoretical optimum and provide expected constraints for the high-luminosity upgrade of the Large Hadron Collider. While we primarily concentrate on the Higgs trilinear self-coupling, we also consider constraints on other SMEFT operators that affect off-shell Higgs production. |
| title | Neural simulation-based inference of the Higgs trilinear self-coupling via off-shell Higgs production |
| topic | High Energy Physics - Phenomenology High Energy Physics - Experiment Data Analysis, Statistics and Probability Machine Learning |
| url | https://arxiv.org/abs/2507.02032 |