Identification of new M 31 star cluster candidates from PAndAS images using convolutional neural networks
Abstract
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.
- Publication:
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Astronomy and Astrophysics
- Pub Date:
- February 2022
- DOI:
- 10.1051/0004-6361/202142169
- arXiv:
- arXiv:2111.07798
- Bibcode:
- 2022A&A...658A..51W
- Keywords:
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- galaxies: star clusters: general;
- galaxies: star clusters: individual: M 31;
- Astrophysics - Astrophysics of Galaxies
- E-Print:
- Published in A&