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

TL;DR

Higgsformer is a transformer-based model that classifies Higgs events directly from raw detector hits, bypassing traditional reconstruction, and achieves competitive performance with standard methods.

Contribution

This work demonstrates that a transformer model can be retrained to classify Higgs events directly from raw hits, eliminating the need for intermediate physics object reconstruction.

Findings

Higgsformer achieves an AUC of 0.855 from raw hits.

It matches traditional pipeline performance at 40% b-tagging efficiency.

The approach is effective under varying dataset sizes and pileup levels.

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.