1. Prospects for annihilating dark matter from M31 and M33 observations with the Cherenkov Telescope ArrayMiltiadis Michailidis, Lorenzo Marafatto, Denys Malyshev, Fabio Iocco, Gabrijela Zaharijas, Olga Sergijenko, Maria Isabel Bernardos, Christopher Eckner, Alexey Boyarsky, Anastasia Sokolenko, Andrea Santangelo, 2023, original scientific article Abstract: Abstract
M31 and M33 are the closest spiral galaxies and the largest members (together with the Milky Way) of the Local group, which makes them interesting targets for indirect dark matter searches. In this paper we present studies of the expected sensitivity of the Cherenkov Telescope Array (CTA) to an annihilation signal from weakly interacting massive particles from M31 and M33. We show that a 100 h long observation campaign will allow CTA to probe annihilation cross-sections up to 〈συ〉 ≈ 5·10-25 cm3 s-1 for the τ
+
τ
- annihilation channel (for M31, at a DM mass of 0.3 TeV), improving the current limits derived by HAWC by up to an order of magnitude.
We present an estimate of the expected CTA sensitivity, by also taking into account the contributions of the astrophysical background and other possible sources of systematic uncertainty.
We also show that CTA might be able to detect the extended emission from the bulge of M31, detected at lower energies by the Fermi/LAT. Keywords: dark matter, gamma rays, Cherenkov Telescope Array, i Published in RUNG: 13.01.2025; Views: 284; Downloads: 10
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2. AutoSourceID-Classifier : star-galaxy classification using a convolutional neural network with spatial informationF. Stoppa, Saptashwa Bhattacharyya, R. Ruiz de Austri, P. Vreeswijk, S. Caron, Gabrijela Zaharijas, S. Bloemen, G. Principe, Denys 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: 1932; Downloads: 7
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3. Investigating the VHE gamma-ray sources using deep neural networksVeronika Vodeb, Saptashwa Bhattacharyya, G. Principe, Gabrijela Zaharijas, R. Austri, F. Stoppa, S. Caron, Denys Malyshev, 2023, published scientific conference contribution Abstract: The upcoming Cherenkov Telescope Array (CTA) will dramatically improve the point-source sensitivity compared to the current Imaging Atmospheric Cherenkov Telescopes (IACTs). One of the key science projects of CTA will be a survey of the whole Galactic plane (GPS) using both southern and northern observatories, specifically focusing on the inner galactic region. We extend a deep learning-based image segmentation software pipeline (autosource-id) developed on Fermi-LAT data to detect and classify extended sources for the simulated CTA GPS. Using updated instrument response functions for CTA (Prod5), we test this pipeline on simulated gamma-ray sources lying in the inner galactic region (specifically 0∘Keywords: deep neural network, cosmic-rays, CTA, classification Published in RUNG: 31.08.2023; Views: 2911; Downloads: 7
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