| Title: | AutoSourceID-Light : Fast optical source localization via U-Net and Laplacian of Gaussian |
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| Authors: | ID Stoppa, F. (Author)
ID Vreeswijk, P. (Author)
ID Bloemen, S. (Author)
ID Bhattacharyya, Saptashwa, University of Nova Gorica (Author)
ID Caron, S (Author)
ID Jóhannesson, G. (Author)
ID Ruiz de Austri, R. (Author)
ID Van den Oetelaar, C. (Author)
ID Zaharijas, Gabrijela, University of Nova Gorica (Author)
ID Groot, P.J. (Author)
ID Cator, E. (Author)
ID Nelemans, G. (Author) |
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| Files: |
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| Language: | English |
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| Work type: | Not categorized |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | UNG - University of Nova Gorica
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| 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. |
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| Keywords: | astronomical databases, data analysis, image processing |
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| Year of publishing: | 2022 |
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| Number of pages: | 8 |
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| Numbering: | A109, 662 |
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| PID: | 20.500.12556/RUNG-7891 |
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| COBISS.SI-ID: | 139051267 |
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| DOI: | https://doi.org/10.1051/0004-6361/202243250 |
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| NUK URN: | URN:SI:UNG:REP:G9JZPD1X |
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| Publication date in RUNG: | 23.01.2023 |
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| Views: | 2281 |
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| Downloads: | 0 |
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