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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: 881; Prenosov: 4
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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: 833; Prenosov: 8
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AutoSourceID-Light : Fast optical source localization via U-Net and Laplacian of Gaussian
F. Stoppa, P. Vreeswijk, S. Bloemen, Saptashwa Bhattacharyya, S Caron, G. Jóhannesson, R. Ruiz de Austri, C. Van den Oetelaar, Gabrijela Zaharijas, P.J. Groot, E. Cator, G. Nelemans, 2022, izvirni znanstveni članek

Opis: Aims: With the ever-increasing survey speed of optical wide-field telescopes and the importance of discovering transients when they are still young, rapid and reliable source localization is paramount. We present AutoSourceID-Light (ASID-L), an innovative framework that uses computer vision techniques that can naturally deal with large amounts of data and rapidly localize sources in optical images. Methods: We show that the ASID-L algorithm based on U-shaped networks and enhanced with a Laplacian of Gaussian filter provides outstanding performance in the localization of sources. A U-Net network discerns the sources in the images from many different artifacts and passes the result to a Laplacian of Gaussian filter that then estimates the exact location. Results: Using ASID-L on the optical images of the MeerLICHT telescope demonstrates the great speed and localization power of the method. We compare the results with SExtractor and show that our method outperforms this more widely used method rapidly detects more sources not only in low and mid-density fields, but particularly in areas with more than 150 sources per square arcminute. The training set and code used in this paper are publicly available.
Ključne besede: astronomical databases, data analysis, image processing
Objavljeno v RUNG: 23.01.2023; Ogledov: 1600; Prenosov: 0
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Long-term ozone data analysis
Vjera Butković, Tomislav Cvitaš, Katja Džepina, Nenad Kezele, Leo Klasinc, 2002, izvirni znanstveni članek

Opis: Various approaches to the analysis of 10-year continuous ozone monitoring from the EUROTRAC-TOR network station Puntijarka near Zagreb are reported. The site has a rural character (45.90degrees N; 15.97degrees E, 980 m a.s.l.) and is representative of the lower troposphere of a wider region. Mean hourly ozone volume fractions measured from 1990-1999, autocorrelation plots for all data and for data for summer periods (May-Sep.), box and whiskers representations of diurnal variations during winter (Nov.-March) and summer periods, mean monthly values and 12-month moving averages, and the Fourier transform of the complete set of 94,248 hourly mean ozone volume fractions are discussed. The data show no increase, or possibly a slight decrease, of the ozone volume fraction toward the end of the decade.
Ključne besede: long term ozone data, fourier analysis, ozone trend analysis, Puntijarka field station
Objavljeno v RUNG: 12.04.2021; Ogledov: 2353; Prenosov: 0
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7.
Technological and analytical review of contact tracing apps for COVID-19 management
Rajan Gupta, Gaurav Pandey, Poonam Chaudhary, Saibal K. Pal, 2021, izvirni znanstveni članek

Opis: Role of technology is improving for COVID-19 management all around the world. Usage of mobile applications, web applications, cloud computing, and related technologies have helped many public administrators worldwide manage the current pandemic. Contact tracing applications are such mobile app solutions that are used by more than 100 countries today. This study presents a structured research review-based framework related to multiple contact tracing applications. The various components of the framework are related to technological working, design architecture, and feature analysis of the applications, along with the analysis of the acceptance of such applications worldwide. Also, components focusing on the security features and analysis of these applications based on Data Privacy, Security Vetting, and different attacks have been included in the research framework. Many applications are yet to explore the analytical capabilities of the data generated through contact tracing. The various use-cases identified for these applications are detecting positive case probability, identifying a containment zone in the country, finding regional hotspots, monitoring public events & gatherings, identifying sensitive routes, and allocating resources in various regions during the pandemic. This study will act as a guide for the users researching contact tracings applications using the proposed four-layered framework for their app assessment.
Ključne besede: novel corona virus, location technology, contact tracing applications, Aarogya Setu App, data science, data analysis
Objavljeno v RUNG: 02.04.2021; Ogledov: 2216; Prenosov: 0
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Observational evidence in favor of scale-free evolution of sunspot groups
Alexander Shapoval, Jean-Louis Le Mouël, M. Shnirman, Vincent Courtillot, 2018, izvirni znanstveni članek

Ključne besede: sunspots, sun, magnetic fields, data analysis
Objavljeno v RUNG: 23.03.2021; Ogledov: 2271; Prenosov: 56
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