Repozitorij Univerze v Novi Gorici

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Naslov:Localisation and classification of gamma ray sources using neural networks
Avtorji:ID Oetelaar, Chris van den (Avtor)
ID Bhattacharyya, Saptashwa (Avtor)
ID Panes, Boris (Avtor)
ID Caron, Sascha (Avtor)
ID Zaharijas, Gabrijela (Avtor)
ID Ruiz de Austri, Roberto (Avtor)
ID Jóhannesson, Guõlaugur (Avtor)
Datoteke:URL https://pos.sissa.it/395/663/pdf
 
.pdf ICRC2021_663.pdf (997,42 KB)
MD5: 1AF7F3E79DF10697558A73D85F38CCA6
 
Jezik:Angleški jezik
Vrsta gradiva:Neznano
Tipologija:1.08 - Objavljeni znanstveni prispevek na konferenci
Organizacija:UNG - Univerza v Novi Gorici
Opis:With limited statistics and spatial resolution of current detectors, accurately localising and separating gamma-ray point sources from the dominating interstellar emission in the GeV energy range is challenging. Motivated by the challenges of the traditional methods used for the gamma-ray source detection, here we demonstrate the application of deep learning based algorithms to automatically detect and classify point sources, which can be applied directly to the binned Fermi-LAT data and potentially be generalised to other wavelengths. For the point source detection task, we use popular deep neural network structure U-NET, together with image segmentation, for precise localisation of sources, various clustering algorithms were tested on the segmented images. The training samples are based on the source properties of AGNs and PSRs from the latest Fermi-LAT source catalog, in addition to the background interstellar emission. Finally, we have created a more complex but robust training data generation exploiting full detector potential, increasing spatial resolution at the highest energies.
Ključne besede:gamma-rays, deep learning, computer vision
Leto izida:2021
Št. strani:str. 1-9
Številčenje:663
PID:20.500.12556/RUNG-6833 Novo okno
COBISS.SI-ID:78766339 Novo okno
UDK:539.1
ISSN pri članku:1824-8039
NUK URN:URN:SI:UNG:REP:X86XV3JX
Datum objave v RUNG:01.10.2021
Število ogledov:1830
Število prenosov:42
Metapodatki:XML RDF-CHPDL DC-XML DC-RDF
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Gradivo je del zbornika

Naslov:37th International Cosmic Ray Conference [also] ICRC2021
COBISS.SI-ID:69435907 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

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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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