Denoising Graph Super-Resolution towards Improved Collider Event Reconstruction

TL;DR

This paper introduces a software-based super-resolution method using transformers to enhance calorimeter granularity and noise suppression in collider experiments, improving particle reconstruction without physical detector modifications.

Contribution

It presents a novel transformer-based super-resolution approach integrated into collider reconstruction pipelines, advancing software techniques for detector data enhancement.

Findings

Super-resolution improves reconstruction quality significantly.

Transformer-based model enhances interpretability.

Method is applicable without physical detector changes.

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 super-resolution an attractive alternative. This study explores integrating super-resolution 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 without physical changes to the detector. To demonstrate its impact, we propose a novel transformer-based particle flow model that offers improved particle reconstruction quality and interpretability. Our results demonstrate that super-resolution can be readily applied at collider experiments.