| Title: | Localisation and classification of gamma ray sources using neural networks |
|---|
| Authors: | ID Oetelaar, Chris van den (Author)
ID Bhattacharyya, Saptashwa (Author)
ID Panes, Boris (Author)
ID Caron, Sascha (Author)
ID Zaharijas, Gabrijela (Author)
ID Ruiz de Austri, Roberto (Author)
ID Jóhannesson, Guõlaugur (Author) |
|---|
| Files: |  https://pos.sissa.it/395/663/pdf
 ICRC2021_663.pdf (997,42 KB)
MD5: 1AF7F3E79DF10697558A73D85F38CCA6
|
|---|
| Language: | English |
|---|
| Work type: | Unknown |
|---|
| Typology: | 1.08 - Published Scientific Conference Contribution |
|---|
| Organization: | UNG - University of Nova Gorica
|
|---|
| Abstract: | 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. |
|---|
| Keywords: | gamma-rays, deep learning, computer vision |
|---|
| Year of publishing: | 2021 |
|---|
| Number of pages: | str. 1-9 |
|---|
| Numbering: | 663 |
|---|
| PID: | 20.500.12556/RUNG-6833  |
|---|
| COBISS.SI-ID: | 78766339 |
|---|
| UDC: | 539.1 |
|---|
| ISSN on article: | 1824-8039 |
|---|
| NUK URN: | URN:SI:UNG:REP:X86XV3JX |
|---|
| Publication date in RUNG: | 01.10.2021 |
|---|
| Views: | 1673 |
|---|
| Downloads: | 42 |
|---|
| Metadata: |     |
|---|
|
:
|
Copy citation |
|---|
| | | | Average score: | (0 votes) |
|---|
| Your score: | Voting is allowed only for logged in users. |
|---|
| Share: |  |
|---|
Hover the mouse pointer over a document title to show the abstract or click
on the title to get all document metadata. |
