  The Standard Model (SM) Higgs boson, the most recently discovered elementary
particle, may still serve as a mediator between the SM sector and a new physics
sector related to dark matter (DM). The Large Hadron Collider (LHC) has not yet
fully constrained the physics associated with the Higgs boson, leaving room for
such possibilities. Among the various potential mass scales of the dark sector,
the sub-GeV mass range is particularly intriguing. This parameter space
presents significant challenges for DM direct detection experiments that rely
on nuclear recoils. Various innovative experimental methods are currently under
investigation to explore this sub-GeV dark sector. The LHC, functioning as a
Higgs factory, could explore this sector once the challenge of identifying DM
signals is resolved. Due to the significantly lower mass of particles in the
dark sector compared to the Higgs boson, these particles are expected to be
highly boosted following the Higgs boson's decay. However, detecting and
identifying these highly boosted particles remains a considerable challenge at
the LHC, despite their eventual decay into SM particles. We employ a
well-motivated leptophobic $Z^{\prime}_B$ model as a prototype to analyze the
distinctive signatures from Higgs boson exotic decays into multi-photons. These
signatures consist of collimated photons that fail to meet the photon isolation
criteria, forming jet-like objects. Conventional analyses relying solely on the
purity of energy deposits in the electromagnetic calorimeter would fail to
detect these signatures, as they would be overwhelmed by background events from
Quantum Chromodynamics. To effectively distinguish between such novel signal
signatures and SM background events, we leverage advanced machine learning
techniques, specifically the transformer encoder in a multimodal network
structure.
