20.500.12556/RUNG-6833
Localisation and classification of gamma ray sources using neural networks
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.
gamma-rays
deep learning
computer vision
true
true
false
Angleški jezik
Ni določen
Neznano
2021-10-01 09:34:13
2021-10-01 10:01:50
2023-09-27 03:07:25
0000-00-00 00:00:00
2021
0
0
str. 1-9
663
2021
0000-00-00
NiDoloceno
NiDoloceno
NiDoloceno
0000-00-00
0000-00-00
0000-00-00
78766339
539.1
1824-8039
69435907
URN:SI:UNG:REP:X86XV3JX
https://pos.sissa.it/395/663/pdf
1
https://repozitorij.ung.si/Dokument.php?lang=slv&id=22905
ICRC2021_663.pdf
ICRC2021_663.pdf
1
1AF7F3E79DF10697558A73D85F38CCA6
270e952479379735496e35297a7cfa3c4502d4c209298ea9af5e17e76b7f719e
15af985b-05d0-11ee-9c48-5ef991fed68f
https://repozitorij.ung.si/Dokument.php?lang=slv&id=22904
Univerza v Novi Gorici
0
0
0