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1.
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: 433; Prenosov: 9
.pdf Celotno besedilo (990,51 KB)

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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: 469; Prenosov: 5
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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: 555; Prenosov: 3
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6.
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: 681; 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: 644; Prenosov: 7
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8.
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: 745; Prenosov: 5
.pdf Celotno besedilo (554,96 KB)
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Expected exclusion limits to TeV dark matter from the perseus cluster with the Cherenkov Telescope Array
Rémi Adam, Saptashwa Bhattacharyya, Judit Pérez Romero, Samo Stanič, Veronika Vodeb, Serguei Vorobiov, Danilo Zavrtanik, Marko Zavrtanik, Miha Živec, 2023, objavljeni znanstveni prispevek na konferenci

Opis: Clusters of galaxies are the largest gravitationally-bound structures in the Universe. They are composed of galaxies and gas (approximately 15% of the total mass) mostly dark matter (DM, accounts up to 85% of the total mass). If the DM is composed of Weakly Interacting Massive Particles (WIMPs), galaxy clusters represent one of the best targets to search for gamma-ray signals induced by the decay of WIMPs, with masses around the TeV scale. Due to its sensitivity and energy range of operation (from 20 GeV to 300 TeV), the Cherenkov Telescope Array (CTA) Observatory has a unique opportunity to test WIMPs with masses close to the unitarity limit. This will complement the searches for DM from other gamma-ray observatories as well as direct and collider experiments. The CTA Observatory is planning to search for gamma-ray emission, either its origin may be cosmic-ray (CR) or DM related, in the Perseus galaxy cluster during the first years of operation. In this poster, we will present the software created to perform the analysis using the ctools software and the corresponding results.
Ključne besede: Cherenkov Telescope Array, CTA, dark matter, standard model, dwarf spheroidal galaxies
Objavljeno v RUNG: 26.09.2023; Ogledov: 758; Prenosov: 4
.pdf Celotno besedilo (1,33 MB)
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10.
Sensitivity of the Cherenkov Telescope Array to the gamma-ray emission from neutrino sources detected by IceCube
Olga Sergijenko, Saptashwa Bhattacharyya, Judit Pérez Romero, Samo Stanič, Veronika Vodeb, Serguei Vorobiov, Danilo Zavrtanik, Marko Zavrtanik, Miha Živec, 2023, objavljeni znanstveni prispevek na konferenci

Opis: Gamma-ray observations of the astrophysical neutrino sources are fundamentally important for understanding the underlying neutrino production mechanism. We investigate the Cherenkov Telescope Array (CTA) ability to detect the very-high-energy (VHE) gamma-ray counterparts to the neutrino-emitting Active Galaxies. The CTA performance under different configurations and array layouts is computed based on the neutrino and gamma-ray simulations of steady and transient types of sources, assuming that the neutrino events are detected with the IceCube neutrino telescope. The CTA detection probability is calculated for both CTA sites taking into account the visibility constraints. We find that, under optimal observing conditions, CTA could observe the VHE gamma-ray emission from at least 3 neutrino events per year.
Ključne besede: Cherenkov Telescope Array, IceCube neutrino telescope, neutrinos, neutrino sources
Objavljeno v RUNG: 26.09.2023; Ogledov: 828; Prenosov: 6
.pdf Celotno besedilo (1,08 MB)
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