Hits to Higgs: Hit-Level Higgs Classification from Raw LHC Detector Data Using Higgsformer

Abstract

We present Higgsformer, a transformer-based architecture that classifies Higgs events at the Large Hadron Collider directly from raw inner tracker hits, bypassing the traditional reconstruction chain of intermediate physics objects. As a benchmark, we focus on distinguishing $t\bar{t}H$ from $t\bar{t}$ events with $H \to b\bar{b}$, a particularly challenging task due to their similar final state topologies. Our pipeline begins with event generation in Pythia8, fast simulation with ACTS/Fatras, and classification directly from raw detector hits. We show for the first time that a transformer model originally developed for inner tracker hit-to-track assignment can be retrained to classify Higgs signal events directly from raw hits. For comparison, we reconstruct the same events with Delphes and train a Particle Transformer as an object-based classifier. We evaluate both approaches under varying dataset sizes and pileup levels. Despite relying exclusively on inner tracker hits, our large Higgsformer achieves an AUC of $0.855$, matching the performance of the traditional reconstruction pipeline at a $b$-tagging efficiency of $\approx 40\%$ under the same detector constraints.