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Title:Investigating the VHE gamma-ray sources using deep neural networks
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)
Files:.pdf ICRC2023_599.pdf (962,45 KB)
MD5: 4BB15D6D19CECEA732CA9C8BEB5BB46F
 
URL https://pos.sissa.it/444/599/
 
Language:English
Work type:Unknown
Typology:1.08 - Published Scientific Conference Contribution
Organization:UNG - University of Nova Gorica
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.
Keywords:deep neural network, cosmic-rays, CTA, classification
Publication date:01.01.2023
Year of publishing:2023
Number of pages:str. 1-8
Numbering:599, 444
PID:20.500.12556/RUNG-8374-5750e5df-2ff1-1e60-8d2d-91b5f0da6158 New window
COBISS.SI-ID:162825987 New window
UDC:539.1
ISSN on article:1824-8039
DOI:https://doi.org/10.22323/1.444.0599 New window
NUK URN:URN:SI:UNG:REP:WWF6IUJB
Publication date in RUNG:31.08.2023
Views:1060
Downloads:6
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Record is a part of a proceedings

Title:38th International Cosmic Ray Conference [also] ICRC2023
COBISS.SI-ID:162195971 New window

Record is a part of a journal

Title:Proceedings of science
Shortened title:Pos proc. sci.
Publisher:Sissa
ISSN:1824-8039
COBISS.SI-ID:20239655 New window

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License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.

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