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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, original scientific article

Abstract: 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.
Keywords: astronomical databases, data analysis, statistics, image processing
Published in RUNG: 12.12.2023; Views: 662; Downloads: 4
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7.
The Cherenkov Telescope Array
Daniel Mazin, Christopher Eckner, Gašper Kukec Mezek, Samo Stanič, Serguei Vorobiov, Lili Yang, Gabrijela Zaharijas, Danilo Zavrtanik, Marko Zavrtanik, Lukas Zehrer, 2019, published scientific conference contribution

Abstract: The Cherenkov Telescope Array (CTA) is the next generation ground-based observatory for gamma-ray astronomy at very-high energies. It will be capable of detecting gamma rays in the energy range from 20 GeV to more than 300 TeV with unprecedented precision in energy and directional reconstruction. With more than 100 telescopes of three different types it will be located in the northern hemisphere at La Palma, Spain, and in the southern at Paranal, Chile. CTA will be one of the largest astronomical infrastructures in the world with open data access and it will address questions in astronomy, astrophysics and fundamental physics in the next decades. In this presentation we will focus on the status of the CTA construction, the status of the telescope prototypes and highlight some of the physics perspectives.
Keywords: very-high-energy gamma-ray astronomy, Cherenkov Telescope Array, CTA sensitivity, gamma-ray bursts, POpulation Synthesis Theory Integrated project for very high-energy emission
Published in RUNG: 04.12.2023; Views: 690; Downloads: 3
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8.
POSyTIVE : a GRB population study for the Cherenkov Telescope Array
Maria Grazia Bernardini, Christopher Eckner, Gašper Kukec Mezek, Samo Stanič, Serguei Vorobiov, Lili Yang, Gabrijela Zaharijas, Danilo Zavrtanik, Marko Zavrtanik, Lukas Zehrer, 2019, published scientific conference contribution

Abstract: One of the central scientific goals of the next-generation Cherenkov Telescope Array (CTA) is the detection and characterization of gamma-ray bursts (GRBs). CTA will be sensitive to gamma rays with energies from about 20 GeV, up to a few hundred TeV. The energy range below 1 TeV is particularly important for GRBs. CTA will allow exploration of this regime with a ground-based gamma-ray facility with unprecedented sensitivity. As such, it will be able to probe radiation and particle acceleration mechanisms at work in GRBs. In this contribution, we describe POSyTIVE, the POpulation Synthesis Theory Integrated project for very high-energy emission. The purpose of the project is to make realistic predictions for the detection rates of GRBs with CTA, to enable studies of individual simulated GRBs, and to perform preparatory studies for time-resolved spectral analyses. The mock GRB population used by POSyTIVE is calibrated using the entire 40-year dataset of multi-wavelength GRB observations. As part of this project we explore theoretical models for prompt and afterglow emission of long and short GRBs, and predict the expected radiative output. Subsequent analyses are performed in order to simulate the observations with CTA, using the publicly available ctools and Gammapy frameworks. We present preliminary results of the design and implementation of this project.
Keywords: very-high-energy gamma-ray astronomy, Cherenkov Telescope Array, CTA sensitivity, gamma-ray bursts, population Synthesis Theory, very high-energy emission
Published in RUNG: 04.12.2023; Views: 889; Downloads: 1
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9.
Cherenkov Telescope Array Science : a multi-wavelength and multi-messenger perspective
Ulisses Barres de Almeida, Christopher Eckner, Gašper Kukec Mezek, Samo Stanič, Serguei Vorobiov, Lili Yang, Gabrijela Zaharijas, Danilo Zavrtanik, Marko Zavrtanik, Lukas Zehrer, 2019, published scientific conference contribution

Abstract: The Cherenkov Telescope Array (CTA) will be the major global observatory for VHE gamma-ray astronomy over the next decade and beyond. It will be an explorer of the extreme universe, with a broad scientific potential: from understanding the role of relativistic cosmic particles, to the search for dark matter. Covering photon energies from 20 GeV to 300 TeV, and with an angular resolution unique in the field, of about 1 arc min, CTA will improve on all aspects of the performance with respect to current instruments, surveying the high energy sky hundreds of times faster than previous TeV telescopes, and with a much deeper view. The very large collection area of CTA makes it an important probe of transient phenomena. The first CTA telescope has just been inaugurated in the Canary Islands, Spain, and as more telescopes are added in the coming years, scientific operation will start. It is evident that CTA will have important synergies with many of the new generation astronomical and astroparticle observatories. In this talk we will review the CTA science case from the point of view of its synergies with other instruments and facilities, highlighting the CTA needs in terms of external data, as well as the opportunities and strategies for cooperation to achieve the basic CTA science goals.
Keywords: very-high-energy gamma-ray astronomy, Cherenkov Telescope Array, CTA performances, transient VHE sources, CTA science
Published in RUNG: 04.12.2023; Views: 914; Downloads: 4
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10.
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, original scientific article

Abstract: 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.
Keywords: data analysis, image processing, astronomical databases
Published in RUNG: 08.11.2023; Views: 626; Downloads: 7
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