Machine Learning Enhanced Detection of Higgs Chain Decays in Vector Boson Fusion

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 $4.5\sigma$, for an integrated luminosity of 300 fb$^{-1}$ at the CERN machine.