Hunting the Unseen: Deep Learning Analysis for Semi-Visible Jet Tagging
Semi-Visible Jets (SVJs) constitute a distinctive collider signature of strongly interacting dark sectors, embedding Dark Matter candidates, wherein jets contain both visible Standard Model objects and invisible dark hadrons, giving rise to correlated jet activity and missing transverse momentum. In this work, we investigate SVJs produced through a heavy Z' mediator and perform an study over a representative set of benchmark scenarios spanning different mediator masses and dark sector parameters in the context of so-called Hidden Valley Models. To characterise the signal, we combine global event kinematics with jet substructure observables, including the primary Lund Jet Plane (LJP), the two-point energy correlation, angularity, and charged hadron multiplicity. These representations are used to train five Deep Learning classifiers for SVJ vs standard jet discrimination: a Vision Transformer operating on LJP images, a JetLOV network based on a hierarchical clustering tree, a Multi-Layer Perceptron using high level observables, and two multimodal networks that combine the image-based or hierarchical representations of the radiation pattern with the high jet-level observables. This enables a direct combination of global kinematics, radiation patterns, and jet clustering structure. We find that global kinematic observables outperform the LJP and hierarchical jet representations, with the latter providing stronger discrimination than LJP images. Combining these complementary representations with global kinematics yields the best overall performance. More broadly, this study shows that unlocking the full discovery potential of SVJs would benefit from going beyond global kinematics to exploit the rich information encoded in their internal structure, providing a benchmark for future searches at the Large Hadron Collider.