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The paper applies super-resolution and a transformer-based particle-flow model to LHC-style calorimeter reconstruction, with ML as the core method rather than a side tool. Its main claim is that the software preprocessing improves reconstruction at fixed granularity but does not fully match a finer detector, and the authors present it as a proof of concept. For AI readers, it's a useful example of ML being used to compensate for expensive hardware in scientific instruments, with the caveat that gains are partial and demonstrated in simulation-style pipelines.
Denoising Graph Super-Resolution towards Improved Collider Event Reconstruction
arXiv:2409.16052v3 Announce Type: replace-cross Abstract: In preparation for Higgs factories and energy-frontier facilities, future colliders are moving toward high-granularity calorimeters to improve reconstruction quality. However, the cost and construction complexity of such detectors is substantial, making software-based approaches like superresolution an attractive complementary tool. This study explores integrating superresolution techniques into an LHC-like reconstruction pipeline to effectively enhance calorimeter granularity and suppress noise. We find that this software preprocessing step significantly improves reconstruction quality at fixed detector granularity, narrowing the performance gap to a finer calorimeter without closing it. To demonstrate its impact, we propose a novel transformer-based particle-flow model that offers improved particle reconstruction quality and interpretability. Our results serve as a proof of concept, demonstrating the potential of superresolution to enhance reconstruction in collider environments.
Abstract:In preparation for Higgs factories and energy-frontier facilities, future colliders are moving toward high-granularity calorimeters to improve reconstruction quality. However, the cost and construction complexity of such detectors is substantial, making software-based approaches like superresolution an attractive complementary tool. This study explores integrating superresolution techniques into an LHC-like reconstruction pipeline to effectively enhance calorimeter granularity and suppress noise. We find that this software preprocessing step significantly improves reconstruction quality at fixed detector granularity, narrowing the performance gap to a finer calorimeter without closing it. To demonstrate its impact, we propose a novel transformer-based particle-flow model that offers improved particle reconstruction quality and interpretability. Our results serve as a proof of concept, demonstrating the potential of superresolution to enhance reconstruction in collider environments.
High Energy Physics - Experiment (hep-ex); Machine Learning (cs.LG)
arXiv:2409.16052 [hep-ex]
(or arXiv:2409.16052v3 [hep-ex] for this version)
https://doi.org/10.48550/arXiv.2409.16052 Focus to learn more arXiv-issued DOI via DataCite
Phys. Rev. D 114, 072010 (2026)
https://doi.org/10.1103/2bjm-w5yq Focus to learn more DOI(s) linking to related resources
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