"Hadron-in-fat-jet'' AI Tagging to Detect Rare Decays Such as W^(pm)toπ^(pm)γ

We investigate a novel class of boosted-object signatures at the LHC, where a high-$p_{\text{T}}$ fat-jet contains an identifiable hadron or quarkonium state originating from rare or semi-exclusive decays. Unlike conventional boosted jet studies, which focus on multi-prong partonic substructure, our approach probes hybrid configurations such as $W^{\pm}\to\pi^{\pm}\gamma$, where a localized hadronic or quarkonium signal is embedded within a collimated jet. By fine-tuning the signature-oriented, pre-trained Sophon AI model optimized for large-radius jets, and combining it with an event-level BDT and a soft-drop-mass shape fit, we obtain an expected 95\% CL upper limit of ${\cal B}(W^{\pm}\to\pi^{\pm}\gamma)<2.78\times10^{-5}$ for $450\,\mathrm{fb}^{-1}$ in our nominal setup. This study serves as a first proof-of-principle demonstration of the ``hadron-in-fat-jet'' paradigm; substantial gains in sensitivity are expected from improved trigger strategies, additional production channels, and dedicated taggers, while the methodology itself is broadly applicable to a wide range of rare Standard Model processes and searches for light or exotic resonances at present and future collider experiments.