Repozitorij Univerze v Novi Gorici

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Naslov:Investigating the VHE gamma-ray sources using deep neural networks
Avtorji:ID Vodeb, Veronika (Avtor)
ID Bhattacharyya, Saptashwa (Avtor)
ID Principe, G. (Avtor)
ID Zaharijas, Gabrijela (Avtor)
ID Austri, R. (Avtor)
ID Stoppa, F. (Avtor)
ID Caron, S. (Avtor)
ID Malyshev, D. (Avtor)
Datoteke:.pdf ICRC2023_599.pdf (962,45 KB)
MD5: 4BB15D6D19CECEA732CA9C8BEB5BB46F
 
URL https://pos.sissa.it/444/599/
 
Jezik:Angleški jezik
Vrsta gradiva:Neznano
Tipologija:1.08 - Objavljeni znanstveni prispevek na konferenci
Organizacija:UNG - Univerza v Novi Gorici
Opis: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.
Ključne besede:deep neural network, cosmic-rays, CTA, classification
Datum objave:01.01.2023
Leto izida:2023
Št. strani:str. 1-8
Številčenje:599, 444
PID:20.500.12556/RUNG-8374-5750e5df-2ff1-1e60-8d2d-91b5f0da6158 Novo okno
COBISS.SI-ID:162825987 Novo okno
UDK:539.1
ISSN pri članku:1824-8039
DOI:https://doi.org/10.22323/1.444.0599 Novo okno
NUK URN:URN:SI:UNG:REP:WWF6IUJB
Datum objave v RUNG:31.08.2023
Število ogledov:923
Število prenosov:6
Metapodatki:XML RDF-CHPDL DC-XML DC-RDF
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Vaša ocena:Ocenjevanje je dovoljeno samo prijavljenim uporabnikom.
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Gradivo je del zbornika

Naslov:38th International Cosmic Ray Conference [also] ICRC2023
COBISS.SI-ID:162195971 Novo okno

Gradivo je del revije

Naslov:Proceedings of science
Skrajšan naslov:Pos proc. sci.
Založnik:Sissa
ISSN:1824-8039
COBISS.SI-ID:20239655 Novo okno

Licence

Licenca:CC BY-NC-ND 4.0, Creative Commons Priznanje avtorstva-Nekomercialno-Brez predelav 4.0 Mednarodna
Povezava:http://creativecommons.org/licenses/by-nc-nd/4.0/deed.sl
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