The Bulletin of The Korean Astronomical Society (천문학회보)
- Volume 44 Issue 2
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- Pages.74.2-74.2
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- 2019
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- 1226-2692(pISSN)
Velocity Dispersion Bias of Galaxy Groups classified by Machine Learning Algorithm
- Lee, Youngdae (Department of Astronomy and Space Science, Chungnam National Nuniversity (CNU)) ;
- Jeong, Hyunjin (Korea Astronomy and Space Science Institute (KASI)) ;
- Ko, Jongwan (Korea Astronomy and Space Science Institute (KASI)) ;
- Lee, Joon Hyeop (Korea Astronomy and Space Science Institute (KASI)) ;
- Lee, Jong Chul (Korea Astronomy and Space Science Institute (KASI)) ;
- Lee, Hye-Ran (Korea Astronomy and Space Science Institute (KASI)) ;
- Yang, Yujin (Korea Astronomy and Space Science Institute (KASI)) ;
- Rey, Soo-Chang (Department of Astronomy and Space Science, Chungnam National Nuniversity (CNU))
- Published : 2019.10.14
Abstract
We present a possible bias in the estimation of velocity dispersions for galaxy groups due to the contribution of subgroups which are infalling into the groups. We execute a systematic search for flux-limited galaxy groups and subgroups based on the spectroscopic galaxies with r < 17.77 mag of SDSS data release 12, by using DBSCAN (Density-Based Spatial Clustering of Application with Noise) and Hierarchical Clustering Method which are well known unsupervised machine learning algorithm. A total of 2042 groups with at least 10 members are found and ~20% of groups have subgroups. We found that the estimation of velocity dispersions of groups using total galaxies including those in subgroups are underestimated by ~10% compared to the case of using only galaxies in main groups. This result suggests that the subgroups should be properly considered for mass measurement of galaxy groups based on the velocity dispersion.
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