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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.
Keywords: Cherenkov Telescope Array Observatory, dark matter, sub-halos, machine learning, gamma-rays, master's thesis
Published in RUNG: 06.09.2024; Views: 498; Downloads: 8
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Abstract: X-ray absorption spectroscopy of gases is seldom used for structural analysis, except in as much as the extended X-ray absorption fine-structure (EXAFS) signal is used to monitor some specific molecular process. Gas spectroscopy, however, has been the main tool in the systematic study of higher-order photoabsorption processes, multielectron photoexcitations (MEEs), which were introduced with an experiment at the K edge of argon. Along with noble gases, where MEEs are studied in the pure form, free of the structural signal, measurements have been extended to monatomic metal vapours, although at the cost of considerable experimental difficulties. Several types of absorption cells have been devised, among them the ceramic double cell and the heat-pipe cell for experiments in the low-energy region. In a high-temperature oven with considerable technical refinement, edge profiles of nonvolatile metals were measured up to 2500 K. Edge profiles and MEEs, which are strongly dependent on electron correlation, provide sensitive tests of the theory of atomic and molecular structure.
Keywords: X-ray absorption spectroscopy, absorption cells, gas and vapour samples, multielectron photoexcitation, effects of electron correlation
Published in RUNG: 05.09.2024; Views: 518; Downloads: 4
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Abstract: Dogodek v Knjižnici Medvode
Keywords: Urugvaj, video, Branislava Sušnik, humanistična rezidenca
Published in RUNG: 02.09.2024; Views: 483; Downloads: 3
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Abstract: Na predavanju so bile praktično predstavljene bralnomotivacijske strategije M. Sarto in delo z literarnimi besedili.
Keywords: strategije, motiviranje za branje, Malinc, M. Sarto
Published in RUNG: 02.09.2024; Views: 445; Downloads: 3
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Abstract: Pravljice je zbrala in priredila Breda Podbrežnik Vukmir, druga prirediteljica pa je bila pisateljica, pesnica in prevajalka Klarisa Jovanović. Zbirka je izšla pri založbi Malinc pod skrbnim uredniškim vodstvom dr. Barbare Pregelj. Ilustracije je prispevala Mojca Fo. Povod za nastanek knjige je Križnikov pravljični festival, za enega od njih smo namreč zbirali in iskali pravljice o drevesih in gozdovih, saj smo skupaj z Gozdarskim inštitutom pomen biodiverzitete in ohranjanja gozdov prikazali prav s pripovedovanjem takšnih pravljic. »Ob tematskim in motivnem iskanju po svetovni zakladnici sem odkrivala pravljice o drevesih, mnoga od njih so sveta in povezujejo med seboj različne svetove, ter o rastlinah, ki so imele različne pomene v različnih kulturah, posebej pa o rožah. Koliko barv, oblik, skrivnosti in pravljic skrivajo rože, ki rastejo na travnikih, pod drevesi, v senci ali na soncu, po gorskih pobočjih ali na angelskih gredicah!« pravi Breda Podbrežnik
Keywords: pravljice, zbirke, predstavitve, rože
Published in RUNG: 30.08.2024; Views: 479; Downloads: 3
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