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Studentite kao aktivni učesnici vo procesot na učenje
Veronika Piccinini, 2024, drugi sestavni deli

Ključne besede: visoko šolstvo, izobraževanje, univerze, študij
Objavljeno v RUNG: 12.07.2024; Ogledov: 198; Prenosov: 1
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Detection of gamma-ray sources and search for dark matter signals with Cherenkov Telescope Array surveys : dissertation
Veronika Vodeb, 2024, doktorska disertacija

Opis: Gamma rays serve as important messengers in modern astrophysics, offering insights into the most energetic processes in the cosmos. Advancements in gamma-ray astronomy, facilitated by international scientific collaboration, have expanded its reach and capabilities. The Fermi-Large Area Telescope (Fermi-LAT) has so far contributed immensely to our understanding of the gamma-ray sky at GeV energies, surveying numerous source classes. At the same time, ground-based observatories like H.E.S.S., MAGIC, VERITAS, HAWC, and LHASSO, enable the exploration of high-energy (HE) phenomena across various energy scales, reaching the PeV range. The collective data from Fermi-LAT and ground-based instruments provide a comprehensive picture of cosmic phenomena across diverse energy regimes. Efforts to catalog HE gamma-ray sources have resulted in the detection of several thousand sources at GeV, including Pulsar Wind Nebulae (PWNe), Supernova Remnants (SNRs), pulsars, blazars, and Gamma-Ray Bursts (GRBs), with the observational capability to study their spectral and spatial morphology enhancing our understanding of their origin and evolution. Looking ahead, the Cherenkov Telescope Array (CTA) represents the next frontier in ground-based gamma-ray astronomy. Operating at very high energies (VHE) between 20 GeV and 300 TeV, CTA's improved sensitivity, angular resolution, and expanded field of view (FoV) promise enhanced imaging of extended sources and performance of large-scale surveys. CTA's Key Science Projects (KSPs) include the Extragalactic (EGAL) survey, a survey of a quarter of the extragalactic sky, and the Galactic Plane Survey (GPS), a survey of the entire Galactic Plane (GP). The KSPs will receive dedicated observation time and careful planning to ensure the optimization of their scientific output. As CTA is currently entering the construction phase, simulations are being extensively employed to predict its response to various signals, playing a vital role in comprehending CTA's response and sensitivity to different signals. The derived predictions are paving the way for estimating the CTA's scientific output, informing the observational strategy, and ensuring its success in maximizing the contribution to HE gamma-ray astronomy. In this thesis, I contribute to assessing the sensitivity of the CTA surveys, particularly the GPS and the EGAL survey, to diverse astrophysical sources and signals. Focusing on the GPS, I delve into understanding the detectability of pulsar halos, which emit multi-TeV gamma rays, the detection of which was recently reported by the HAWC Observatory. The study involves a spatial-spectral likelihood analysis, evaluating sensitivity to simple Gaussian extended sources and physically modeled sources. Employing a template-fitting approach, I analyze CTA's GPS sensitivity to extended sources and explore the prospects for pulsar halo detection and characterization. A preliminary population study addresses the visibility of pulsar halos to CTA's GPS and explores the angular sensitivity to extended sources. The thesis sets the detectability prospects of pulsar halos with CTA and investigates what fraction of the preliminary pulsar halo population CTA will be able to probe. The thesis extends its exploration into the persistent mystery of dark matter (DM), a fundamental puzzle in cosmology. The search for DM signals remains a vigorous pursuit in the physics community, utilizing various astrophysical messengers resulting from DM particle annihilation or decay. I investigate the potential of CTA's GPS to detect dark sub-halos within our galaxy, utilizing a similar approach as in the sensitivity assessment to pulsar halos, applied to recent sub-halo population simulations. Furthermore, the thesis addresses the intricate task of disentangling DM components from astrophysical contributions in the observed gamma-ray sky. In terms of the EGAL survey, employing advanced statistical methods such as the cross-correlation technique, I explore the prospects of using CTA's EGAL survey to correlate the Extragalactic Gamma-ray Background (EGRB) with galaxy catalogs, providing insights into DM properties. While traditional methods rely on likelihood analysis with background subtraction or template fitting, the emergence of supervised machine learning (ML) offers a novel, potentially more effective approach for cataloging the sky. The thesis touches upon the usability of ML in the high and VHE gamma-ray sky. My study focuses on CTA's GPS and utilizes deep-learning-based algorithms in a detection pipeline for the automatic classification of extended sources from gamma-ray data. As CTA stands at the forefront of gamma-ray astronomy as the next-generation observatory, the research presented in this thesis contributes a small step towards answering the open questions about pulsar halos and DM, showcasing the potential breakthroughs that may emerge from CTA's observations. The detailed likelihood analysis performed aims to advance our understanding of these enigmas, from the physical intricacies of pulsar halos to the elusive nature of DM, driven by curiosity about the continuous exploration of the Universe's mysteries.
Ključne besede: high-energy gamma-ray astronomy, astroparticle physics, Cherenkov Telescope Array, pulsar halos, dark matter, dissertations
Objavljeno v RUNG: 06.06.2024; Ogledov: 361; Prenosov: 5
.pdf Celotno besedilo (36,25 MB)

Pregled pridelave grozdja in vina žlahtne vinske trte (Vitis vinifera l.) 'pinela' in 'zelen' v Vipavski dolini : diplomsko delo
Veronika Kos, 2024, diplomsko delo

Opis: V tej diplomski nalogi smo s pomočjo anketiranja pridelovalcev pregledali, na kakšen način se prideluje grozdje in vino pri sortah Vitis Vinifera L. 'Zelen' in 'Pinela' v zgornji Vipavski dolini. Pomembno je, da razumemo, da ti dve sorti rasteta na flišni podlagi in pod submediteranskim podnebjem. Ampelografski opis nam razloži, kako ti dve sorti ločimo od ostalih. V nadaljevanju smo s pomočjo anketiranja pridelovalcev teh dveh sort dobili vpogled v način pridelave grozdja, tipe rezi, uporabo ukrepa razlistanja in ugotovili, da je večina vinogradov obrnjenih na vzhod ali zahod. V povprečju dosega mošt/grozdje sorte 'Zelen' višji pH kot sorta 'Pinela', kar tudi vzporedno pojasni, da ima 'Zelen' nižje kisline. Količina pridelanega grozdja se vsako leto povečuje, v grafu lahko vidimo enakomerno rast pri sorti 'Zelen', nekoliko manj enakomerno pa pri sorti 'Pinela', kar je posledica zunanjih dejavnikov. Iz ankete je razvidno, da se tako pri maceraciji kot tudi pri skladiščenju največkrat uporabljajo posode iz nerjavečega jekla, nekateri pa uporabljajo tudi lesene sode.
Ključne besede: diplomske naloge, pinela, zelen, Vipavska dolina, pridelava, vino, grozdje
Objavljeno v RUNG: 21.03.2024; Ogledov: 759; Prenosov: 15
.pdf Celotno besedilo (990,51 KB)

Razmišljaj širše - spoznaj možnosti univerzitetnega študija čez mejo
Veronika Piccinini, 2024, drugi sestavni deli

Ključne besede: študij, visoko šolstvo, izobraževanje, univerza
Objavljeno v RUNG: 12.02.2024; Ogledov: 681; Prenosov: 6
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Iskustvo so prekugranično studiranje
Veronika Piccinini, 2023, drugi sestavni deli

Ključne besede: študiј, univerze, študijski programi, visoko šolstvo, izobraževanje
Objavljeno v RUNG: 15.01.2024; Ogledov: 715; Prenosov: 3
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AutoSourceID-Classifier : star-galaxy classification using a convolutional neural network with spatial information
F. Stoppa, Saptashwa Bhattacharyya, R. Ruiz de Austri, P. Vreeswijk, S. Caron, Gabrijela Zaharijas, S. Bloemen, G. Principe, D. Malyshev, Veronika Vodeb, 2023, izvirni znanstveni članek

Opis: Aims: Traditional star-galaxy classification techniques often rely on feature estimation from catalogs, a process susceptible to introducing inaccuracies, thereby potentially jeopardizing the classification’s reliability. Certain galaxies, especially those not manifesting as extended sources, can be misclassified when their shape parameters and flux solely drive the inference. We aim to create a robust and accurate classification network for identifying stars and galaxies directly from astronomical images. Methods: The AutoSourceID-Classifier (ASID-C) algorithm developed for this work uses 32x32 pixel single filter band source cutouts generated by the previously developed AutoSourceID-Light (ASID-L) code. By leveraging convolutional neural networks (CNN) and additional information about the source position within the full-field image, ASID-C aims to accurately classify all stars and galaxies within a survey. Subsequently, we employed a modified Platt scaling calibration for the output of the CNN, ensuring that the derived probabilities were effectively calibrated, delivering precise and reliable results. Results: We show that ASID-C, trained on MeerLICHT telescope images and using the Dark Energy Camera Legacy Survey (DECaLS) morphological classification, is a robust classifier and outperforms similar codes such as SourceExtractor. To facilitate a rigorous comparison, we also trained an eXtreme Gradient Boosting (XGBoost) model on tabular features extracted by SourceExtractor. While this XGBoost model approaches ASID-C in performance metrics, it does not offer the computational efficiency and reduced error propagation inherent in ASID-C’s direct image-based classification approach. ASID-C excels in low signal-to-noise ratio and crowded scenarios, potentially aiding in transient host identification and advancing deep-sky astronomy.
Ključne besede: astronomical databases, data analysis, statistics, image processing
Objavljeno v RUNG: 12.12.2023; Ogledov: 910; Prenosov: 4
.pdf Celotno besedilo (10,31 MB)
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AutoSourceID-FeatureExtractor : optical image analysis using a two-step mean variance estimation network for feature estimation and uncertainty characterisation
F. Stoppa, R. Ruiz de Austri, P. Vreeswijk, Saptashwa Bhattacharyya, S. Caron, S. Bloemen, Gabrijela Zaharijas, G. Principe, Veronika Vodeb, P. J. Groot, E. Cator, G. Nelemans, 2023, izvirni znanstveni članek

Opis: Aims: In astronomy, machine learning has been successful in various tasks such as source localisation, classification, anomaly detection, and segmentation. However, feature regression remains an area with room for improvement. We aim to design a network that can accurately estimate sources' features and their uncertainties from single-band image cutouts, given the approximated locations of the sources provided by the previously developed code AutoSourceID-Light (ASID-L) or other external catalogues. This work serves as a proof of concept, showing the potential of machine learning in estimating astronomical features when trained on meticulously crafted synthetic images and subsequently applied to real astronomical data. Methods: The algorithm presented here, AutoSourceID-FeatureExtractor (ASID-FE), uses single-band cutouts of 32x32 pixels around the localised sources to estimate flux, sub-pixel centre coordinates, and their uncertainties. ASID-FE employs a two-step mean variance estimation (TS-MVE) approach to first estimate the features and then their uncertainties without the need for additional information, for example the point spread function (PSF). For this proof of concept, we generated a synthetic dataset comprising only point sources directly derived from real images, ensuring a controlled yet authentic testing environment. Results: We show that ASID-FE, trained on synthetic images derived from the MeerLICHT telescope, can predict more accurate features with respect to similar codes such as SourceExtractor and that the two-step method can estimate well-calibrated uncertainties that are better behaved compared to similar methods that use deep ensembles of simple MVE networks. Finally, we evaluate the model on real images from the MeerLICHT telescope and the Zwicky Transient Facility (ZTF) to test its transfer learning abilities.
Ključne besede: data analysis, image processing, astronomical databases
Objavljeno v RUNG: 08.11.2023; Ogledov: 875; Prenosov: 8
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Performance study update of observations in divergent mode for the Cherenkov Telescope Array
A. Donini, Saptashwa Bhattacharyya, Judit Pérez Romero, Samo Stanič, Veronika Vodeb, Serguei Vorobiov, Danilo Zavrtanik, Marko Zavrtanik, Miha Živec, 2023, objavljeni znanstveni prispevek na konferenci

Opis: Due to the limited field of view (FoV) of Cherenkov telescopes, the time needed to achieve target sensitivity for surveys of the extragalactic and Galactic sky is large. To optimize the time spent to perform such surveys, a so-called “divergent mode” of the Cherenkov Telescope Array Observatory (CTAO) was proposed as an alternative observation strategy to the traditional parallel pointing. In the divergent mode, each telescope points to a position in the sky that is slightly offset, in the outward direction, from the original center of the field of view. This bring the advantage of increasing the total instantaneous arrays’ FoV. From an enlarged field of view also benefits the search for very-high-energy transient sources, making it possible to cover large sky regions in follow-up observations, or to quickly cover the probability sky map in case of Gamma Ray Bursts (GRB), Gravitational Waves (GW), and other transient events. In this contribution, we present the proposed implementation of the divergent pointing mode and its first preliminary performance estimation for the southern CTAO array.
Ključne besede: Cherenkov Telescope Array, CTAO, divergent mode, very-high-energy transient sources
Objavljeno v RUNG: 26.09.2023; Ogledov: 975; Prenosov: 5
.pdf Celotno besedilo (554,96 KB)
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