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Machine Learning in Gamma Astronomy

arXiv 2501.19064 ↗First posted 31 Jan 2025astro-ph.IM astro-ph.HE cs.LGcited by 0

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Abstract (arXiv)

The purpose of this paper is to review the most popular deep learning methods used to analyze astroparticle data obtained with Imaging Atmospheric Cherenkov Telescopes and provide references to the original papers.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 7 of its 54 references that are in the map. First posted on arXiv 31 Jan 2025.

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7 of 54 references.

References · 7

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
  3. 2021Deep learning with photosensor timing information as a background rejection method for the Cherenkov Telescope Arraycited by 2
  4. 2021First Full-Event Reconstruction from Imaging Atmospheric Cherenkov Telescope Real Data with Deep Learningcited by 2
  5. 2022Energy Reconstruction in Analysis of Cherenkov Telescopes Images in TAIGA Experiment Using Deep Learning Methodscited by 2
  6. 2022Using a Conditional Generative Adversarial Network to Control the Statistical Characteristics of Generated Images for IACT Data Analysiscited by 2
  7. 2022Using conditional variational autoencoders to generate images from atmospheric Cherenkov telescopescited by 1

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