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LAMOST helps to propose new method searching for clusters in Andromeda galaxy

Making use of the LAMOST spectra data as the training sample, a new method to search for star clusters in the Andromeda galaxy has recently been proposed by the research team led by Dr. Shoucheng Wang and Prof. Jun Ma from National Astronomical Observatories of Chinese Academy of Sciences (NAOC). With this method, the researchers identified 117 new high-confidence cluster candidates in the Andromeda galaxy based on PAndAS (The Pan-Andromeda Archaeological Survey) data, among which 109 are young cluster candidates located in the disk, and 8 are old globular clusters in the outer halo.


This work is a breakthrough in using data from ground-based telescopes (LAMOST, PAndAS, etc.) to search for star clusters in the Andromeda galaxy, and has been published in Astronomy & Astrophysics.


Star clusters, such as young open clusters, old globular clusters, and young massive clusters, are widely distributed throughout the galaxy from the bulge and disk to the outer halo, providing an excellent tool for revealing the early formation and evolutionary history of galaxies.


The Andromeda Galaxy, also known as Messier 31 (M31), is the closest large spiral galaxy to our Milky Way, and is an ideal laboratory to study galaxy formation and evolution. Astronomers have long been working on the identification of star clusters in M31 to obtain a complete cluster sample of this galaxy. Recent wide-field photometric and spectroscopic surveys have provided a good opportunity to search for M31 clusters. However, to find and identify the special objects we need from tens of millions of images obtained by the deep wide-field photometric surveys is difficult at present.


By selecting 346 M31 clusters, as well as Galactic foreground objects and background galaxies, from LAMOST DR6 database, and combining with the cluster and non-cluster samples in M31 obtained from the literature as training samples, the researchers constructed a class of two-channel deep convolutional neural network (CNN) model to identify star clusters. Its accuracy has been proved to be able to achieve 99% in the test set. Using this model, the researchers identified 117 new high-confidence M31 cluster candidates from more than 21 million images obtained by the PAndAS photometric survey (Figure).


A more complete M31 star cluster sample is a valuable reference for further study on the formation and evolution of M31. The automatic detection of star clusters in large sky surveys is in urgent demand considering the forthcoming wide field large aperture facilities, and the CNN model proposed by this work is very timely. Moreover, this method can also be generated to a wider range of applications. For example, it is helpful to identify gravitational lenses and search for high redshift galaxies.


Figure: Spatial distribution of the newly identified M31 cluster candidates in M31. The blue Y symbol is the center of M31. (Credit: Shoucheng Wang)
The paper can be accessed at: