Issue |
A&A
Volume 680, December 2023
|
|
---|---|---|
Article Number | A109 | |
Number of page(s) | 16 | |
Section | Numerical methods and codes | |
DOI | https://doi.org/10.1051/0004-6361/202347576 | |
Published online | 18 December 2023 |
AutoSourceID-Classifier
Star-galaxy classification using a convolutional neural network with spatial information
1
Department of Astrophysics/IMAPP, Radboud University,
PO Box 9010,
6500 GL
Nijmegen,
The Netherlands
e-mail: f.stoppa@astro.ru.nl
2
Center for Astrophysics and Cosmology, University of Nova Gorica,
Vipavska 13,
5000
Nova Gorica,
Slovenia
3
High Energy Physics/IMAPP, Radboud University,
PO Box 9010,
6500 GL
Nijmegen,
The Netherlands
4
Nikhef,
Science Park 105,
1098 XG
Amsterdam,
The Netherlands
5
Instituto de Física Corpuscular, IFIC-UV/CSIC,
Valencia,
Spain
6
Department of Mathematics/IMAPP, Radboud University,
PO Box 9010,
6500 GL
Nijmegen,
The Netherlands
7
SRON, Netherlands Institute for Space Research,
Sorbonnelaan 2,
3584 CA
Utrecht,
The Netherlands
8
Institute of Astronomy, KU Leuven,
Celestijnenlaan 200D,
3001
Leuven,
Belgium
9
Institute for Fundamental Physics of the Universe,
Via Beirut 2,
34151
Trieste,
Italy
10
Department of Astronomy and Inter-University Institute for Data Intensive Astronomy, University of Cape Town,
Private Bag X3,
Rondebosch
7701,
South Africa
11
South African Astronomical Observatory,
PO Box 9,
Observatory,
Cape Town
7935,
South Africa
12
Dipartimento di Fisica, Universitá di Trieste,
34127
Trieste,
Italy
13
Istituto Nazionale di Fisica Nucleare,
Sezione di Trieste,
34127
Trieste,
Italy
14
Erlangen Centre for Astroparticle Physics,
Nikolaus-Fiebiger-Str. 2,
Erlangen
91058,
Germany
Received:
26
July
2023
Accepted:
17
October
2023
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.
Key words: methods: data analysis / techniques: image processing / astronomical databases: miscellaneous / stars: imaging / Galaxies: statistics
© The Authors 2023
Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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