Machine Learning Enhanced Detection of Higgs Chain Decays in Vector Boson Fusion
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arXiv
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| Natura: | Preprint |
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2026
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| _version_ | 1866917550321827840 |
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| author | Chowdhury, Shreecheta Chakraborty, Amit Moretti, Stefano |
| author_facet | Chowdhury, Shreecheta Chakraborty, Amit Moretti, Stefano |
| contents | Over the years, Vector Boson Fusion (VBF) has established itself as one of the most robust production channels for studying the Higgs boson, while also serving as a promising pathway for exploring potential signatures of physics Beyond the Standard Model (BSM) at the Large Hadron Collider (LHC). Following the discovery of a SM-like Higgs boson, new opportunities have arisen to also investigate heavy resonances that decay into SM-like Higgs boson pairs, $hh$, thereby offering valuable insights into the structure of the Higgs sector and the dynamics governing Electro-Weak Symmetry Breaking (EWSB). In this work, we analyze a final state involving, alongside 2 forward/backward light quarks, 4 $b$-quarks emerging from the chain decay $h_2\to h_1h_1\to b\bar b b\bar b$ wherein the heavy CP-even Higgs state $h_2$ is produced in the VBF process $qq\to qqh_2$ and belongs to the Next-to-Minimal Supersymmetric SM (NMSSM). This BSM scenario is used as an illustrative example of the potential of using only low-level calorimeter information enhanced by advanced Deep Learning (DL) methodologies in searching for this channel, which can achieve a statistical significance of approximately $4.5σ$, for an integrated luminosity of 300 fb$^{-1}$ at the CERN machine. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2606_00695 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Machine Learning Enhanced Detection of Higgs Chain Decays in Vector Boson Fusion Chowdhury, Shreecheta Chakraborty, Amit Moretti, Stefano High Energy Physics - Phenomenology High Energy Physics - Experiment Over the years, Vector Boson Fusion (VBF) has established itself as one of the most robust production channels for studying the Higgs boson, while also serving as a promising pathway for exploring potential signatures of physics Beyond the Standard Model (BSM) at the Large Hadron Collider (LHC). Following the discovery of a SM-like Higgs boson, new opportunities have arisen to also investigate heavy resonances that decay into SM-like Higgs boson pairs, $hh$, thereby offering valuable insights into the structure of the Higgs sector and the dynamics governing Electro-Weak Symmetry Breaking (EWSB). In this work, we analyze a final state involving, alongside 2 forward/backward light quarks, 4 $b$-quarks emerging from the chain decay $h_2\to h_1h_1\to b\bar b b\bar b$ wherein the heavy CP-even Higgs state $h_2$ is produced in the VBF process $qq\to qqh_2$ and belongs to the Next-to-Minimal Supersymmetric SM (NMSSM). This BSM scenario is used as an illustrative example of the potential of using only low-level calorimeter information enhanced by advanced Deep Learning (DL) methodologies in searching for this channel, which can achieve a statistical significance of approximately $4.5σ$, for an integrated luminosity of 300 fb$^{-1}$ at the CERN machine. |
| title | Machine Learning Enhanced Detection of Higgs Chain Decays in Vector Boson Fusion |
| topic | High Energy Physics - Phenomenology High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2606.00695 |