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Naslov:AutoSourceID-Classifier : star-galaxy classification using a convolutional neural network with spatial information
Avtorji:ID Stoppa, F. (Avtor)
ID Bhattacharyya, Saptashwa (Avtor)
ID Ruiz de Austri, R. (Avtor)
ID Vreeswijk, P. (Avtor)
ID Caron, S. (Avtor)
ID Zaharijas, Gabrijela (Avtor)
ID Bloemen, S. (Avtor)
ID Principe, G. (Avtor)
ID Malyshev, D. (Avtor)
ID Vodeb, Veronika (Avtor)
Datoteke: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
 
Jezik:Angleški jezik
Vrsta gradiva:Neznano
Tipologija:1.01 - Izvirni znanstveni članek
Organizacija:UNG - Univerza v Novi Gorici
Opis: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.
Ključne besede:astronomical databases, data analysis, statistics, image processing
Datum objave:01.01.2023
Leto izida:2023
Št. strani:str. 1-16
Številčenje:Vol. , [article no.] ǂ
PID:20.500.12556/RUNG-8689-850d6590-87bb-98c6-420e-99621b7b0521 Novo okno
COBISS.SI-ID:177027843 Novo okno
UDK:52
ISSN pri članku:1432-0746
DOI:10.1051/0004-6361/202347576 Novo okno
NUK URN:URN:SI:UNG:REP:994RZKRN
Datum objave v RUNG:12.12.2023
Število ogledov:772
Število prenosov:4
Metapodatki:XML RDF-CHPDL DC-XML DC-RDF
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Gradivo je del revije

Naslov:Astronomy & astrophysics
Skrajšan naslov:Astron. astrophys.
Založnik:EDP Sciences
ISSN:1432-0746
COBISS.SI-ID:392577 Novo okno

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Licenca:CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
Povezava:http://creativecommons.org/licenses/by/4.0/deed.sl
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