| Title: | Search for a signal from dark matter sub-halos with the galactic plane survey of CTA Observatory : master's thesis |
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| Authors: | ID Rokavec, Zoja (Author)
ID Zaharijas, Gabrijela (Mentor) More about this mentor...  |
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| Files: |  Zoja_Rokavec.pdf (5,39 MB)
MD5: F6BDAC82965737B3CFF83437143B1124
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| Language: | English |
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| Work type: | Master's thesis/paper |
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| Typology: | 2.09 - Master's Thesis |
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| Organization: | FN - School of Science
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| 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. |
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| Keywords: | Cherenkov Telescope Array Observatory, dark matter, sub-halos, machine learning, gamma-rays, master's thesis |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Place of publishing: | Nova Gorica |
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| Place of performance: | Nova Gorica |
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| Publisher: | Z. Rokavec |
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| Year of publishing: | 2024 |
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| Year of performance: | 2024 |
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| Number of pages: | VIII, 53 str. |
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| COBISS.SI-ID: | 206663683 |
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| UDC: | 52 |
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| NUK URN: | URN:SI:UNG:REP:EEC5AEES |
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| Publication date in RUNG: | 06.09.2024 |
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| Views: | 252 |
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| Downloads: | 1 |
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