Identification of new M31 star cluster candidates from PAndAS images using convolutional neural networks

Abstract

Context.Identification of new star cluster candidates in M31 is fundamental for the study of the M31 stellar cluster system. The machine-learning method convolutional neural network (CNN) is an efficient algorithm for searching for new M31 star cluster candidates from tens of millions of images from wide-field photometric surveys. Aims.We search for new M31 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 M31 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 M31 cluster candidates. The CNN predictions were finally checked by five experienced human inspectors to obtain high-confidence M31 star cluster candidates. Results.After the inspection, we identified a catalogue of 117 new M31 cluster candidates. Most of the new candidates are young clusters that are located in the M31 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 M31 halo and exhibit features similar to those of confirmed halo globular clusters. The projected distances to the M31 centre for three of them are larger than 100\,kpc.

Published in A&A

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Cited by in corpus (1)

• Evaluating the feasibility of interpretable machine learning for globular cluster detection