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Title:Search for a signal from dark matter sub-halos with the galactic plane survey of CTA Observatory : master's thesis
Authors:ID Rokavec, Zoja (Author)
ID Zaharijas, Gabrijela (Mentor) More about this mentor... New window
Files:.pdf Zoja_Rokavec.pdf (5,39 MB)
MD5: F6BDAC82965737B3CFF83437143B1124
 
Language:English
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FN - School of Science
Abstract:Dark matter (DM), known to be a dominant matter component in the Universe, has been searched for extensively, yet remains undetected. One of the promising avenues of detecting a DM signal is to observe the so called ’DM sub-halos’ within our galaxy. These sub-halos, which are numerous within the Milky Way, are formed by the clustering of DM, as predicted by cosmological simulations, and most of them lack baryonic matter counterparts, making them challenging to detect. How- ever, the annihilation or decay of Weakly Interacting Massive Particles (WIMPs), a leading candidate for DM, within these sub-halos is expected to produce very high-energy (VHE) photons (called gamma-rays) at TeV energies, offering possible indirect DM detection. In this thesis, we focus on the Galactic Plane Survey (GPS) of the Cherenkov Tele- scope Array Observatory (CTAO), an upcoming ground-based gamma-ray obser- vatory, which promises unprecedented sensitivity and resolution in the detection of cosmic gamma-ray sources in the ∼ 30 GeV to ∼ 100 TeV energy range. As dark sub-halos are expected to appear as unidentified (point) sources in the CTAO GPS data, we employ a machine learning (ML)-based approach, the AutoSour- ceID framework, leveraging U-shaped networks (U-Nets) and Laplacian of Gaus- sian (LoG) filter, for automatic source detection and localization, and apply it to simulated GPS data. We establish detection thresholds for U-Nets trained on dif- ferently scaled counts (counts, square root or log of counts) and identify which approach offers best results (in terms of flux sensitivity and location accuracy). Our findings suggest that using log-scaled counts yields a factor of 1.7 lower flux threshold compared to counts alone. In addition, we compare our ML outcomes with traditional methods; however, this comparison is not straightforward, as ML and traditional approaches fundamentally differ in their methodologies and un- derlying assumptions. Nevertheless, The flux threshold obtained using log-scaled counts is comparable to that of the traditional likelihood-based detection method implemented in the Gammapy library, although further study is needed to estab- lish a more definitive comparison. These preliminary results also suggest that the flux threshold for detecting 90% of true sources with the ML approach is approx- imately two times lower than the sensitivity reported for the GPS in the CTAO publication. Although these results are not directly comparable due to differences in methodology, they hint that ML methods may offer superior performance in certain scenarios. Furthermore, we discuss the implications of our results on the sensitivity to DM sub-halos, improving it by a factor of 4, highlighting the possi- bility of detecting at least one sub-halo with a cross section approximately ⟨σv⟩ = 2.4 × 10−23 cm3 /s.
Keywords:Cherenkov Telescope Array Observatory, dark matter, sub-halos, machine learning, gamma-rays, master's thesis
Publication status:Published
Publication version:Version of Record
Place of publishing:Nova Gorica
Place of performance:Nova Gorica
Publisher:Z. Rokavec
Year of publishing:2024
Year of performance:2024
Number of pages:VIII, 53 str.
PID:20.500.12556/RUNG-9284-7f6db7e1-6a81-448b-78a5-13ae5461c198 New window
COBISS.SI-ID:206663683 New window
UDC:52
NUK URN:URN:SI:UNG:REP:EEC5AEES
Publication date in RUNG:06.09.2024
Views:634
Downloads:12
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:03.09.2024

Secondary language

Language:Slovenian
Title:Magistrsko delo


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