| Title: | Investigating the VHE gamma-ray sources using deep neural networks |
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| Authors: | ID Vodeb, Veronika (Author)
ID Bhattacharyya, Saptashwa (Author)
ID Principe, G. (Author)
ID Zaharijas, Gabrijela (Author)
ID Austri, R. (Author)
ID Stoppa, F. (Author)
ID Caron, S. (Author)
ID Malyshev, D. (Author) |
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| Files: |  ICRC2023_599.pdf (962,45 KB)
MD5: 4BB15D6D19CECEA732CA9C8BEB5BB46F
 https://pos.sissa.it/444/599/
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| Language: | English |
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| Work type: | Unknown |
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| Typology: | 1.08 - Published Scientific Conference Contribution |
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| Organization: | UNG - University of Nova Gorica
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| Abstract: | The upcoming Cherenkov Telescope Array (CTA) will dramatically improve the point-source sensitivity compared to the current Imaging Atmospheric Cherenkov Telescopes (IACTs). One of the key science projects of CTA will be a survey of the whole Galactic plane (GPS) using both southern and northern observatories, specifically focusing on the inner galactic region. We extend a deep learning-based image segmentation software pipeline (autosource-id) developed on Fermi-LAT data to detect and classify extended sources for the simulated CTA GPS. Using updated instrument response functions for CTA (Prod5), we test this pipeline on simulated gamma-ray sources lying in the inner galactic region (specifically 0∘<l<20∘, |b|<4∘) for energies ranging from 30 GeV to 100 TeV.
Dividing the source extensions ranging from 0.03∘ to 1∘ in three different classes, we find that using a simple and light convolutional neural network achieves 97% global accuracy in separating the extended sources from the point-like sources. The neural net architecture including other data pre-processing codes is available online. |
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| Keywords: | deep neural network, cosmic-rays, CTA, classification |
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| Publication date: | 01.01.2023 |
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| Year of publishing: | 2023 |
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| Number of pages: | str. 1-8 |
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| Numbering: | 599, 444 |
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| PID: | 20.500.12556/RUNG-8374-5750e5df-2ff1-1e60-8d2d-91b5f0da6158  |
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| COBISS.SI-ID: | 162825987 |
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| UDC: | 539.1 |
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| ISSN on article: | 1824-8039 |
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| DOI: | https://doi.org/10.22323/1.444.0599 |
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| NUK URN: | URN:SI:UNG:REP:WWF6IUJB |
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| Publication date in RUNG: | 31.08.2023 |
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| Views: | 2306 |
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| Downloads: | 7 |
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| Metadata: |    |
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