| 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: |  ICRC2023_599.pdf (962,45 KB)
MD5: 4BB15D6D19CECEA732CA9C8BEB5BB46F
 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  |
|---|
| COBISS.SI-ID: | 162825987 |
|---|
| UDC: | 539.1 |
|---|
| ISSN on article: | 1824-8039 |
|---|
| DOI: | https://doi.org/10.22323/1.444.0599 |
|---|
| NUK URN: | URN:SI:UNG:REP:WWF6IUJB |
|---|
| Publication date in RUNG: | 31.08.2023 |
|---|
| Views: | 2298 |
|---|
| Downloads: | 7 |
|---|
| 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. |
