Generative Adversarial Network based method for generation of synthetic image parameters for TACTIC <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e607" altimg="si63.svg"><mml:mi>γ</mml:mi></mml:math>-ray telescope-Reference-Cited by-同舟云学术

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Generative Adversarial Network based method for generation of synthetic image parameters for TACTIC γ-ray telescope

Published:2023-07 Issue: Volume:44 Page:100741
ISSN:2213-1337
Container-title:Astronomy and Computing
language:en
Short-container-title:Astronomy and Computing

Author:

Das M.P.,Dhar V.K.,Yadav K.K.

Publisher

Elsevier BV

Subject

Computer Science Applications,Astronomy and Astrophysics,Space and Planetary Science

Reference26 articles.

1. Deep learning using rectified linear units (ReLU);Agarap,2018

2. First detection of a VHE gamma-ray spectral maximum from a cosmic source: Hesscovery of the vela x nebula;Aharonian;Astron. Astrophys.,2006

3. On the parameterization of patterns in the imaging atmospheric cherenkov technique;Akhperjanian;Astropart. Phys.,1999

4. Chandra, P., et al., 2017. Multiwavelength study of VHE emission from Markarian 501 using TACTIC observations during April-May, 2012. 54, 42–51. arXiv:1701.04935. http://dx.doi.org/10.1016/j.newast.2017.01.004.

5. ANN-based energy reconstruction procedure for TACTIC gamma-ray telescope and its comparison with other conventional methods;Dhar;Nucl. Instrum. Methods Phys. Res. A,2009

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1. Ultra-fast generation of air shower images for Imaging Air Cherenkov Telescopes using Generative Adversarial Networks;Journal of Instrumentation;2024-04-01

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