Issue |
A&A
Volume 658, February 2022
|
|
---|---|---|
Article Number | A51 | |
Number of page(s) | 9 | |
Section | Galactic structure, stellar clusters and populations | |
DOI | https://doi.org/10.1051/0004-6361/202142169 | |
Published online | 01 February 2022 |
Identification of new M 31 star cluster candidates from PAndAS images using convolutional neural networks⋆
1
Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012, PR China
e-mail: majun@nao.cas.cn
2
South-Western Institute for Astronomy Research, Yunnan University, Kunming, Yunnan 650091, PR China
e-mail: bchen@ynu.edu.cn
3
School of Astronomy and Space Sciences, University of Chinese Academy of Sciences, Beijing 100049, PR China
4
Yunnan Observatories, Chinese Academy of Sciences, Kunming 650216, PR China
e-mail: longqian@ynao.ac.cn
5
Department of Astronomy, Beijing Normal University, Beijing 100875, PR China
6
National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, PR China
7
School of Computer Science and Engineering, Central South University, Changsha 410083, PR China
8
Key Laboratory of Space Astronomy and Technology, National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012, PR China
Received:
7
September
2021
Accepted:
12
November
2021
Context. Identification of new star cluster candidates in M 31 is fundamental for the study of the M 31 stellar cluster system. The machine-learning method convolutional neural network (CNN) is an efficient algorithm for searching for new M 31 star cluster candidates from tens of millions of images from wide-field photometric surveys.
Aims. We search for new M 31 cluster candidates from the high-quality g- and i-band images of 21 245 632 sources obtained from the Pan-Andromeda Archaeological Survey (PAndAS) through a CNN.
Methods. We collected confirmed M 31 clusters and noncluster objects from the literature as our training sample. Accurate double-channel CNNs were constructed and trained using the training samples. We applied the CNN classification models to the PAndAS g- and i-band images of over 21 million sources to search new M 31 cluster candidates. The CNN predictions were finally checked by five experienced human inspectors to obtain high-confidence M 31 star cluster candidates.
Results. After the inspection, we identified a catalogue of 117 new M 31 cluster candidates. Most of the new candidates are young clusters that are located in the M 31 disk. Their morphology, colours, and magnitudes are similar to those of the confirmed young disk clusters. We also identified eight globular cluster candidates that are located in the M 31 halo and exhibit features similar to those of confirmed halo globular clusters. The projected distances to the M 31 centre for three of them are larger than 100 kpc.
Key words: galaxies: star clusters: general / galaxies: star clusters: individual: M 31
Full Table 2 is only available at the CDS via anonymous ftp to cdsarc.u-strasbg.fr (130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/cat/J/A+A/658/A51
© ESO 2022
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