41. Sensitivity of the Cherenkov Telescope Array to TeV photon emission from the Large Magellanic CloudA. Acharyya, R. Adam, Saptashwa Bhattacharyya, Samo Stanič, Veronika Vodeb, Serguei Vorobiov, Gabrijela Zaharijas, Danilo Zavrtanik, Marko Zavrtanik, Miha Živec, 2023, original scientific article Abstract: A deep survey of the Large Magellanic Cloud at ∼ 0.1−100 TeV photon energies with the Cherenkov Telescope Array is planned.
We assess the detection prospects based on a model for the emission of the galaxy, comprising the four known TeV emitters,
mock populations of sources, and interstellar emission on galactic scales. We also assess the detectability of 30 Doradus and SN 1987A, and the constraints that can be derived on the nature of dark matter. The survey will allow for fine spectral studies of N 157B, N 132D, LMC P3, and 30 Doradus C, and half a dozen other sources should be revealed, mainly pulsar-powered
objects. The remnant from SN 1987A could be detected if it produces cosmic-ray nuclei with a flat power-law spectrum at high energies, or with a steeper index 2.3−2.4 pending a flux increase by a factor > 3−4 over ∼ 2015−2035. Large-scale interstellar emission remains mostly out of reach of the survey if its > 10 GeV spectrum has a soft photon index ∼ 2.7, but degree-scale 0.1 − 10 TeV pion-decay emission could be detected if the cosmic-ray spectrum hardens above >100 GeV. The 30 Doradus star-forming region is detectable if acceleration efficiency is on the order of 1 − 10% of the mechanical luminosity and diffusion is suppressed by two orders of magnitude within < 100 pc. Finally, the survey could probe the canonical velocity-averaged cross section for self-annihilation of weakly interacting massive particles for cuspy Navarro-Frenk-White profiles.
Keywords: very-high energy (VHE) gamma-rays, Cherenkov Telescope Array Observatory, Large Magellanic Cloud, pulsar wind nebulas, galaxiesstar-forming regions, cosmic rays, dark matter
Published in RUNG: 02.06.2023; Views: 1071; Downloads: 1
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42. Sensitivity of the Cherenkov Telescope Array to spectral signatures of hadronic PeVatrons with application to Galactic Supernova RemnantsFabio Acero, Saptashwa Bhattacharyya, Samo Stanič, Veronika Vodeb, Serguei Vorobiov, Gabrijela Zaharijas, Danilo Zavrtanik, Marko Zavrtanik, Miha Živec, 2023, original scientific article Keywords: gamma-rays, cosmic rays, Galactic PeVatrons, Galactic supernova remnants, Cherenkov Telescope Array
Published in RUNG: 14.04.2023; Views: 1105; Downloads: 0
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43. AutoSourceID-Light : Fast optical source localization via U-Net and Laplacian of GaussianF. 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, original scientific article Abstract: 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.
Keywords: astronomical databases, data analysis, image processing
Published in RUNG: 23.01.2023; Views: 1247; Downloads: 0
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44. Localisation and classification of gamma ray sources using neural networksChris van den Oetelaar, Saptashwa Bhattacharyya, Boris Panes, Sascha Caron, Gabrijela Zaharijas, Roberto Ruiz de Austri, Guõlaugur Jóhannesson, 2021, published scientific conference contribution Abstract: With limited statistics and spatial resolution of current detectors, accurately localising and separating gamma-ray point sources from the dominating interstellar emission in the GeV energy range is challenging. Motivated by the challenges of the traditional methods used for the gamma-ray source detection, here we demonstrate the application of deep learning based algorithms to automatically detect and classify point sources, which can be applied directly to the binned Fermi-LAT data and potentially be generalised to other wavelengths. For the point source detection task, we use popular deep neural network structure U-NET, together with image segmentation, for precise localisation of sources, various clustering algorithms were tested on the segmented images. The training samples are based on the source properties of AGNs and PSRs from the latest Fermi-LAT source catalog, in addition to the background interstellar emission. Finally, we have created a more
complex but robust training data generation exploiting full detector potential, increasing spatial resolution at the highest energies.
Keywords: gamma-rays, deep learning, computer vision
Published in RUNG: 01.10.2021; Views: 1696; Downloads: 42
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