Advancing higgsino searches by integrating ML for boosted object tagging and event selection

Higgsinos near the TeV mass range are highly motivated as they offer an elegant solution to the naturalness problem in the Standard Model. Extensive searches for such higgsinos within the framework of General Gauge Mediation (GGM) have been conducted by both the ATLAS and CMS collaborations. However, the sensitivity of these searches in the hadronic channel remains limited, primarily due to the reliance on traditional substructure-based techniques for fat jet identification. In this work, we present a novel search strategy that leverages graph neural networks (GNNs) to improve the characterization of fat jets originating from W / Z / h bosons, top quarks, and QCD-initiated light quarks and gluons. The GNN scores, combined with a boosted decision tree (BDT) classifier, enhance signal-background discrimination, offering a significant improvement in sensitivity for higgsino searches at the LHC.