Identification of new M31 star cluster candidates from PAndAS images using convolutional neural networks. (arXiv:2111.07798v1 [astro-ph.GA])
<a href="http://arxiv.org/find/astro-ph/1/au:+Wang_S/0/1/0/all/0/1">Shoucheng Wang</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Chen_B/0/1/0/all/0/1">Bingqiu Chen</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Ma_J/0/1/0/all/0/1">Jun Ma</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Long_Q/0/1/0/all/0/1">Qian Long</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Yuan_H/0/1/0/all/0/1">Haibo Yuan</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Liu_D/0/1/0/all/0/1">Dezi Liu</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Zhou_Z/0/1/0/all/0/1">Zhimin Zhou</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Liu_W/0/1/0/all/0/1">Wei Liu</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Chen_J/0/1/0/all/0/1">Jiamin Chen</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+He_Z/0/1/0/all/0/1">Zizhao He</a>
Context.Identification of new star cluster candidates in M31 is fundamental
for the study of M31 stellar cluster system. The machine learning method
Convolutional Neural Network (CNN) is an efficient algorithm to search 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 that obtained by the
Pan-Andromeda Archaeological Survey (PAndAS) by CNN. Methods.We collect
confirmed M31 clusters and non cluster objects from the literature as our
training sample. Accurate double-channel CNNs have been constructed and been
trained using the training samples. We have 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 are finally checked by
five experienced inspectors to obtain high confidence M31 star cluster
candidates. Results.After human-inspection, we have identified a catalogue of
117 new M31 cluster candidates. Most of the new candidates are young clusters
being located in the M31 disk. Their morphology, colours and magnitudes are
similar as those of the confirmed young disk clusters. We have also identified
eight globular cluster candidates which are located in the M31 halo and exhibit
features similar as those confirmed halo globular clusters. Three of them have
projected distances to the M31 centre larger than 100,kpc.
Context.Identification of new star cluster candidates in M31 is fundamental
for the study of M31 stellar cluster system. The machine learning method
Convolutional Neural Network (CNN) is an efficient algorithm to search 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 that obtained by the
Pan-Andromeda Archaeological Survey (PAndAS) by CNN. Methods.We collect
confirmed M31 clusters and non cluster objects from the literature as our
training sample. Accurate double-channel CNNs have been constructed and been
trained using the training samples. We have 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 are finally checked by
five experienced inspectors to obtain high confidence M31 star cluster
candidates. Results.After human-inspection, we have identified a catalogue of
117 new M31 cluster candidates. Most of the new candidates are young clusters
being located in the M31 disk. Their morphology, colours and magnitudes are
similar as those of the confirmed young disk clusters. We have also identified
eight globular cluster candidates which are located in the M31 halo and exhibit
features similar as those confirmed halo globular clusters. Three of them have
projected distances to the M31 centre larger than 100,kpc.
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