SR-GAN for SR-gamma: super resolution of photon calorimeter images at collider experiments

Johannes Erdmann, Aaron van der Graaf, Florian Mausolf, Olaf Nackenhorst
8/17/2023
hep-excs.CVphysics.ins-det

No confident code implementation found for this paper. 1 possible match can be shown below.

Abstract

We study single-image super-resolution algorithms for photons at collider experiments based on generative adversarial networks. We treat the energy depositions of simulated electromagnetic showers of photons and neutral-pion decays in a toy electromagnetic calorimeter as 2D images and we train super-resolution networks to generate images with an artificially increased resolution by a factor of four in each dimension. The generated images are able to reproduce features of the electromagnetic showers that are not obvious from the images at nominal resolution. Using the artificially-enhanced images for the reconstruction of shower-shape variables and of the position of the shower center results in significant improvements. We additionally investigate the utilization of the generated images as a pre-processing step for deep-learning photon-identification algorithms and observe improvements in the case of training samples of small size.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 1 weaker match is hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks

85% match1 repo1162017

CaloClouds: Fast Geometry-Independent Highly-Granular Calorimeter Simulation

82% match2023

CaloHadronic: a diffusion model for the generation of hadronic showers

82% match1 repo22025

Enhancing Events in Neutrino Telescopes through Deep Learning-Driven Super-Resolution

80% match1 repo2024

Generative Models for Fast Simulation of Cherenkov Detectors at the Electron-Ion Collider

80% match2 repos32025

SwinCCIR: An end-to-end deep network for Compton camera imaging reconstruction

80% match2025

Cite this paper

@article{erdmann2023srgan,
  title  = {SR-GAN for SR-gamma: super resolution of photon calorimeter images at collider experiments},
  author = {Johannes Erdmann and Aaron van der Graaf and Florian Mausolf and Olaf Nackenhorst},
  year   = {2023},
  eprint = {2308.09025},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2308.09025v2}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2308.09025.svg)](https://codeofpaper.com/paper/2308.09025)

Discussion