First Full-Event Reconstruction from Imaging Atmospheric Cherenkov Telescope Real Data with Deep Learning

Mikaël Jacquemont; Thomas Vuillaume; Alexandre Benoit; Gilles Maurin; Patrick Lambert; Giovanni Lamanna

doi:10.1109/CBMI50038.2021.9461918

First Full-Event Reconstruction from Imaging Atmospheric Cherenkov Telescope Real Data with Deep Learning

Instrumentation and Methods for Astrophysics 2022-03-10 v1 Machine Learning

Authors: Mikaël Jacquemont , Thomas Vuillaume , Alexandre Benoit , Gilles Maurin , Patrick Lambert , Giovanni Lamanna

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Abstract

The Cherenkov Telescope Array is the future of ground-based gamma-ray astronomy. Its first prototype telescope built on-site, the Large Size Telescope 1, is currently under commissioning and taking its first scientific data. In this paper, we present for the first time the development of a full-event reconstruction based on deep convolutional neural networks and its application to real data. We show that it outperforms the standard analysis, both on simulated and on real data, thus validating the deep approach for the CTA data analysis. This work also illustrates the difficulty of moving from simulated data to actual data.

Keywords

cherenkov telescope array imaging atmospheric cherenkov telescopes

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@article{arxiv.2105.14927,
  title  = {First Full-Event Reconstruction from Imaging Atmospheric Cherenkov Telescope Real Data with Deep Learning},
  author = {Mikaël Jacquemont and Thomas Vuillaume and Alexandre Benoit and Gilles Maurin and Patrick Lambert and Giovanni Lamanna},
  journal= {arXiv preprint arXiv:2105.14927},
  year   = {2022}
}

First Full-Event Reconstruction from Imaging Atmospheric Cherenkov Telescope Real Data with Deep Learning

Abstract

Keywords

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