| Title: | AutoSourceID-Classifier : star-galaxy classification using a convolutional neural network with spatial information |
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| Authors: | ID Stoppa, F. (Author)
ID Bhattacharyya, Saptashwa (Author)
ID Ruiz de Austri, R. (Author)
ID Vreeswijk, P. (Author)
ID Caron, S. (Author)
ID Zaharijas, Gabrijela (Author)
ID Bloemen, S. (Author)
ID Principe, G. (Author)
ID Malyshev, D. (Author)
ID Vodeb, Veronika (Author) |
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| Files: |  https://www.aanda.org/component/article?access=doi&doi=10.1051/0004-6361/202347576
 ASID-C.pdf (10,31 MB)
MD5: 6AD1ABC403B960AE561CDBD2D9DE3F34
https://www.aanda.org/component/article?access=doi&doi=10.1051/0004-6361/202347576
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| Language: | English |
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| Work type: | Unknown |
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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: 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. |
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| Keywords: | astronomical databases, data analysis, statistics, image processing |
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| Publication date: | 01.01.2023 |
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| Year of publishing: | 2023 |
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| Number of pages: | str. 1-16 |
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| Numbering: | Vol. , [article no.] ǂ |
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| PID: | 20.500.12556/RUNG-8689-850d6590-87bb-98c6-420e-99621b7b0521  |
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| COBISS.SI-ID: | 177027843 |
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| UDC: | 52 |
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| ISSN on article: | 1432-0746 |
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| DOI: | 10.1051/0004-6361/202347576 |
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| NUK URN: | URN:SI:UNG:REP:994RZKRN |
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| Publication date in RUNG: | 12.12.2023 |
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| Views: | 1513 |
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| Downloads: | 6 |
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