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1.
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, 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: 1192; Downloads: 0
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2.
Localisation and classification of gamma ray sources using neural networks
Chris 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: 1661; Downloads: 42
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