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TeV dark matter search at the Galactic center with the CTA
Gabrijela Zaharijas, unpublished invited conference lecture

Found in: osebi
Keywords: gamma rays, dark matter, Cherenkov Telescope Array
Published: 16.05.2020; Views: 1375; Downloads: 0
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Razumevanje vesolja danes/The evolving view of our universe-a »one step at a time« journey
Gabrijela Zaharijas, other performed works

Found in: osebi
Keywords: dark matter, universe
Published: 16.05.2020; Views: 1412; Downloads: 0
.pdf Fulltext (498,68 KB)

Fermi Large Area Telescope fourth source catalog
S. Abdollahi, F. Acero, M. Ajello, W. B. Atwood, M. Axelsson, L. Baldini, J. Ballet, G. Barbiellini, D. Bastieri, Gabrijela Zaharijas, 2020, original scientific article

Found in: osebi
Keywords: astronomy, gamma rays, sky surveys
Published: 22.05.2020; Views: 1469; Downloads: 85
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High-energy emission from a magnetar giant flare in the Sculptor galaxy
Gabrijela Zaharijas, 2021, original scientific article

Found in: osebi
Keywords: magnetars, gamma-ray astronomy, transients
Published: 14.01.2021; Views: 1004; Downloads: 0
.pdf Fulltext (2,28 MB)

Skrivnosti prav posebnih zvezd, ki jim pravimo magnetarji
Tomaž Zwitter, Nina Manfreda, Gabrijela Zaharijas, 2021, radio or television broadcast

Found in: osebi
Keywords: gama žarki, astrofizika, nevronske zvezde
Published: 26.02.2021; Views: 876; Downloads: 3
URL Fulltext (0,00 KB)

Studying dark matter annihilation in Perseus galaxy cluster using very-high-energy gamma rays
Nemanja Ivković, 2020, master's thesis

Found in: osebi
Keywords: dark matter, gamma-ray astrophysics
Published: 01.03.2021; Views: 1077; Downloads: 0
.pdf Fulltext (7,17 MB)

Sensitivity of the Cherenkov Telescope Array for probing cosmology and fundamental physics with gamma-ray propagation
Serguei Vorobiov, Samo Stanič, Christopher Eckner, R. Adam, A. Acharyya, F. Acero, H. Abe, H. Abdalla, Gabrijela Zaharijas, Marko Zavrtanik, Danilo Zavrtanik, Miha Živec, 2021, original scientific article

Abstract: The Cherenkov Telescope Array (CTA), the new-generation ground-based observatory for γ astronomy, provides unique capabilities to address significant open questions in astrophysics, cosmology, and fundamental physics. We study some of the salient areas of γ cosmology that can be explored as part of the Key Science Projects of CTA, through simulated observations of active galactic nuclei (AGN) and of their relativistic jets. Observations of AGN with CTA will enable a measurement of γ absorption on the extragalactic background light with a statistical uncertainty below 15% up to a redshift z=2 and to constrain or detect γ halos up to intergalactic-magnetic-field strengths of at least 0.3 pG . Extragalactic observations with CTA also show promising potential to probe physics beyond the Standard Model. The best limits on Lorentz invariance violation from γ astronomy will be improved by a factor of at least two to three. CTA will also probe the parameter space in which axion-like particles could constitute a significant fraction, if not all, of dark matter. We conclude on the synergies between CTA and other upcoming facilities that will foster the growth of γ cosmology.
Found in: osebi
Keywords: Cherenkov Telescope Array, active galactic nuclei, gamma-ray experiments, axions, extragalactic magnetic fields
Published: 02.03.2021; Views: 973; Downloads: 35
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Localisation and classification of gamma ray sources using neural networks
Guõlaugur Jóhannesson, Roberto Ruiz de Austri, Gabrijela Zaharijas, Sascha Caron, Boris Panes, Saptashwa Bhattacharyya, Chris van den Oetelaar, 2021, published scientific conference contribution

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.
Found in: osebi
Keywords: gamma-rays, deep learning, computer vision
Published: 01.10.2021; Views: 505; Downloads: 15
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