摘要：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 doublechannel CNNs were constructed and trained using the training samples. We applied the CNN classification models to the PAndAS gand 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.
关键词：galaxies: star clusters: general;galaxies: star clusters: individual: M 31
