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  • Open Access

Denoising graph superresolution toward improved collider event reconstruction

Nilotpal Kakati*, Etienne Dreyer, and Eilam Gross

  • *Contact author: nilotpal.kakati@weizmann.ac.il
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Phys. Rev. D 114, 072010 – Published 28 October, 2026

DOI: https://doi.org/10.1103/2bjm-w5yq

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.

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Outline

Information

Phys. Rev. D 114, 072010– Published 28 October, 2026
  • Received 23 March 2026
  • Revised 10 August 2026
  • Accepted 16 September 2026
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI. Funded by SCOAP3.

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