End-to-end Particle Reconstruction with Transformers in High-Granularity Calorimeters
Abstract
The High-Luminosity upgrade of the Large Hadron Collider (HL-LHC) will drastically increase the number of simultaneously interacting particles, posing a severe challenge to current particle reconstruction methods. While next-generation highly granular calorimeters offer unprecedented 3D measurement capabilities, reconstructing particles from heavily overlapping energy showers remains computationally difficult. This study investigates how raw detector hits can be accurately reconstructed using end-to-end transformer based methods on a simulated calorimeter dataset. We evaluate and compare three distinct machine learning frameworks, focusing on two generative architectures: diffusion and flow matching as well as query-based set prediction. Our results demonstrate that these advanced methods provide highly effective and scalable alternatives for resolving complex overlapping particle showers in conditions encountered in future calorimeters.
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BibTeX
@misc{bao2026endtoend,
title = {{End-to-end Particle Reconstruction with Transformers in High-Granularity Calorimeters}},
author = {Qiming Bao and Haotian Bi and Benedikt Maier and Alex Tapper and Lauri Laatu},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.37798},
eprint = {2610.37798},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.37798},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - End-to-end Particle Reconstruction with Transformers in High-Granularity Calorimeters
AU - Qiming Bao
AU - Haotian Bi
AU - Benedikt Maier
AU - Alex Tapper
AU - Lauri Laatu
PY - 2026
DA - 2026/10/27
PB - arXiv
DO - 10.48550/arxiv.2610.37798
UR - https://arxiv.org/abs/2610.37798
AB - The High-Luminosity upgrade of the Large Hadron Collider (HL-LHC) will drastically increase the number of simultaneously interacting particles, posing a severe challenge to current particle reconstruction methods. While next-generation highly granular calorimeters offer unprecedented 3D measurement capabilities, reconstructing particles from heavily overlapping energy showers remains computationally difficult. This study investigates how raw detector hits can be accurately reconstructed using end-to-end transformer based methods on a simulated calorimeter dataset. We evaluate and compare three distinct machine learning frameworks, focusing on two generative architectures: diffusion and flow matching as well as query-based set prediction. Our results demonstrate that these advanced methods provide highly effective and scalable alternatives for resolving complex overlapping particle showers in conditions encountered in future calorimeters.
ER -