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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2604.20456 |
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| _version_ | 1866910157375537152 |
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| author | Li, Yin Liu, Bingxuan Wang, Jianbin Xie, Jiaqi Xu, Kairong Ye, Ruihan Huang, Zihuan |
| author_facet | Li, Yin Liu, Bingxuan Wang, Jianbin Xie, Jiaqi Xu, Kairong Ye, Ruihan Huang, Zihuan |
| contents | Semi-visible jets (SVJs) provide a characteristic collider signature of strongly interacting dark sectors, in which the key model parameter $r_{\mathrm{inv}}$ controls the fraction of dark hadrons decaying to dark matter candidates. In this work, a regression model is developed to reconstruct $r_{\mathrm{inv}}$ in SVJ events produced in association with an energetic photon. The model uses information from high-level physics objects only, and the training procedure is optimized to ensure applicability. The performance is found to be robust against varying signal parameters and $r_{\mathrm{inv}}$ can be reconstructed at a much higher precision, compared to previously developed analytical method. It offers a new approach to conduct SVJ searches that can potentially unify both $s$-channel and $t$-channel productions, enhancing the sensitivities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_20456 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | How Invisible: Regressing The Key Model Parameter for Semi-visible Jet Searches Li, Yin Liu, Bingxuan Wang, Jianbin Xie, Jiaqi Xu, Kairong Ye, Ruihan Huang, Zihuan High Energy Physics - Phenomenology High Energy Physics - Experiment Semi-visible jets (SVJs) provide a characteristic collider signature of strongly interacting dark sectors, in which the key model parameter $r_{\mathrm{inv}}$ controls the fraction of dark hadrons decaying to dark matter candidates. In this work, a regression model is developed to reconstruct $r_{\mathrm{inv}}$ in SVJ events produced in association with an energetic photon. The model uses information from high-level physics objects only, and the training procedure is optimized to ensure applicability. The performance is found to be robust against varying signal parameters and $r_{\mathrm{inv}}$ can be reconstructed at a much higher precision, compared to previously developed analytical method. It offers a new approach to conduct SVJ searches that can potentially unify both $s$-channel and $t$-channel productions, enhancing the sensitivities. |
| title | How Invisible: Regressing The Key Model Parameter for Semi-visible Jet Searches |
| topic | High Energy Physics - Phenomenology High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2604.20456 |