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Title:AutoSourceID-Classifier : star-galaxy classification using a convolutional neural network with spatial information
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)
Files:URL https://www.aanda.org/component/article?access=doi&doi=10.1051/0004-6361/202347576
 
.pdf ASID-C.pdf (10,31 MB)
MD5: 6AD1ABC403B960AE561CDBD2D9DE3F34
 
URL https://www.aanda.org/component/article?access=doi&doi=10.1051/0004-6361/202347576
 
Language:English
Work type:Unknown
Typology:1.01 - Original Scientific Article
Organization:UNG - University of Nova Gorica
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.
Keywords:astronomical databases, data analysis, statistics, image processing
Publication date:01.01.2023
Year of publishing:2023
Number of pages:str. 1-16
Numbering:Vol. , [article no.] ǂ
PID:20.500.12556/RUNG-8689-850d6590-87bb-98c6-420e-99621b7b0521 New window
COBISS.SI-ID:177027843 New window
UDC:52
ISSN on article:1432-0746
DOI:10.1051/0004-6361/202347576 New window
NUK URN:URN:SI:UNG:REP:994RZKRN
Publication date in RUNG:12.12.2023
Views:1598
Downloads:6
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Record is a part of a journal

Title:Astronomy & astrophysics
Shortened title:Astron. astrophys.
Publisher:EDP Sciences
ISSN:1432-0746
COBISS.SI-ID:392577 New window

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License:CC BY 4.0, Creative Commons Attribution 4.0 International
Link:http://creativecommons.org/licenses/by/4.0/
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

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