  We deploy an advanced Machine Learning (ML) environment, leveraging a
multi-scale cross-attention encoder for event classification, towards the
identification of the $gg\to H\to hh\to b\bar b b\bar b$ process at the High
Luminosity Large Hadron Collider (HL-LHC), where $h$ is the discovered Standard
Model (SM)-like Higgs boson and $H$ a heavier version of it (with $m_H>2m_h$).
In the ensuing boosted Higgs regime, the final state consists of two fat jets.
Our multi-modal network can extract information from the jet substructure and
the kinematics of the final state particles through self-attention transformer
layers. The diverse learned information is subsequently integrated to improve
classification performance using an additional transformer encoder with
cross-attention heads. We ultimately prove that our approach surpasses in
performance current alternative methods used to establish sensitivity to this
process, whether solely based on kinematic analysis or else on a combination of
this with mainstream ML approaches. Then, we employ various interpretive
methods to evaluate the network results, including attention map analysis and
visual representation of Gradient-weighted Class Activation Mapping (Grad-CAM).
Finally, we note that the proposed network is generic and can be applied to
analyse any process carrying information at different scales. Our code is
publicly available for generic use.
