Year: 2026
Type: preprint
Abstract: Real-time jet tagging is critical for identifying short-lived particle decays in the high-throughput detectors of the Large Hadron Collider, where real-time trigger systems responsible for deciding which collision events to store impose strict latency and accuracy constraints. While transformer architectures achieve the highest jet tagging accuracy when compute is unconstrained, their quadratic self-attention cost makes inference restrictive on trigger budget. Existing efficient variants reduce the computational cost, but hinder the classification performance. To address this limitation, we introduce the Patch Hierarchical Attention Transformer (PHAT-JeT), which combines two mechanisms: a physics-inspired geometric message-passing module that encodes local detector-plane structure, and a hierarchical patch-based attention scheme that computes exact attention within small particle groups while preserving global context through lightweight patch-token communication. Within a restricted budget, PHAT-JeT achieves state-of-the-art accuracy and background rejection among all resource-constrained jet tagging models on four benchmarks (\textsc{hls4ml}, JetClass, Top Tagging, and Quark--Gluon). Our code is available at https://github.com/aaronw5/PHAT-JeT.
Source: arXiv (Cornell University)
Language: English
Subfield: Nuclear and High Energy Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
Awards: G1387744893, Collaborative Proposal: Geodynamic Tomography: A New Approach to Studies of Mantle Upwelling and its Application to the Lau Basin and The East Pacific Rise, G2011917337, G2376186588, G3270322552, RESEARCH IN THEORETICAL/EXPERIMENTAL HIGH ENERGY PHYSICS (TASK T-1, T-2 AND P), G5266256302, G5574724152, G6471802762, HDR Institute: Accelerated AI Algorithms for Data-Driven Discovery, RESEARCH IN THEORETICAL/EXPERIMENTAL HIGH ENERGY PHYSICS (TASK T-1, T-2 AND P), G8989177975
OpenAlex ID: W7162085972