HEPTv2: End-to-End Efficient Point Transformer for Charged Particle Reconstruction
Charged-particle tracking -- reconstructing trajectories from sparse detector measurements -- is a fundamental high-energy-physics inference problem and a canonical example of learning under extreme combinatorial ambiguity. At the High-Luminosity Large Hadron Collider (HL-LHC), tracking must remain accurate and efficient despite unprecedented collision densities. Graph neural networks perform strongly, but incur substantial costs from graph construction and processing, while transformer-based approaches rely on auxiliary stages that prevent end-to-end optimization. To address this, we present HEPTv2, an end-to-end point-transformer architecture that reconstructs tracks from detector hits ...
| Authors | Siqi Miao, Shitij Govil, Jack P. Rodgers et al. |
|---|---|
| Primary category | hep-ex |
| Published | 18 June 2026 |
| Last updated at source | 18 June 2026 |
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