Virgo Cluster Membership Based on <i>K</i>-Means Algorithm
- 1 National Research Institute of Astronomy and Geophysics, Cairo, Egypt
- 2 Computer Science Department, Integrated Thebes Institute, Cairo, Egypt
- 3 Math and Computer Science Department, Faculty of Science, Menoufia University, Shibin El Kom, Egypt
- 4 Computer Science Department, Taibah University, Alula Branch, Medina, KSA
Abstract
The Virgo cluster of galaxies is of great importance to study the development of the universe due to its close distance from the earth as well as being the center of the local super cluster. The problem that faces Virgo cluster studies is that it shares the same right ascension (RA) and Declination (DEC) ranges with large number of background as well as foreground galaxies. This study aims to geometrically and statistically estimate Virgo cluster membership. The study employs Virgo cluster data, prepared by Harvard University. The radial velocity (RV) data of the Virgo cluster were treated and employed in exchange of missing galaxies’ third dimension, taking advantage of their proportionality. The data were treated by K -means algorithm, using Matlab 2014, and visual and logical exclusion of extremity galaxies to determine the rational center of the Virgo galaxies cluster. Results were presented, compared and discussed. Finally distances of galaxies from the Virgo cluster center were employed along with normal probability distribution characteristics to identify the most probable Virgo cluster members from the range of Virgo cluster of galaxies. The results showed that out of 17,466 objects surveyed in Virgo galaxy range, only few of galaxies were estimated to be genuine Virgo members.
- Mei, S., et al. (2008) The ACS Virgo Cluster Survey. XIII. SBF Distance Catalog and the Three-Dimensional Structure of the Virgo Cluster. The Astrophysical Journal, 655, 144. https://doi.org/10.1086/509598
- Ouellette, N.N.Q., Courteau, S., Holtzman, J.A., Dalcanton, J.J., McDonald, M. and Zhu, Y. (2014) The Dynamical Properties of Virgo Cluster Disk Galaxies. Structure and Dynamics of Disk Galaxies, 480, 89.
- Maccone, C. (2012) SETI among Galaxies by Virtue of Black Holes. Acta Astronautica, 78, 109-120. https://doi.org/10.1016/j.actaastro.2011.10.011
- Zhu, X. and Chu, Y. (1995) Association of Quasars and Galaxies in the Field of the Virgo Cluster. Chinese Astronomy and Astrophysics, 19, 129-136. https://doi.org/10.1016/0275-1062(95)00018-N
- Andreo, R.B. (2015) Virgo Cluster Galaxies. NASA.
- Yang, X.-S. and Deb, S. (2014) Cuckoo Search: Recent Advances and Applications. Neural Computing and Applications, 24, 169-174. https://doi.org/10.1007/s00521-013-1367-1
- Kaushik, M. and Mathur, B. (2014) Comparative Study of K-Means and Hierarchical Clustering Techniques. International Journal of Software & Hardware Research in Engineering, 2, 93-98.
- Svensson, C.-M., Bondoc, K.G., Pohnert, G. and Figge, M.T. (2017) Segmentation of Clusters by Template Rotation Expectation Maximization. Computer Vision and Image Understanding, 154, 64-72. https://doi.org/10.1016/j.cviu.2016.08.003
- Dong, S., Liu, J., Liu, Y., Zeng, L., Xu, C. and Zhou, T. (2018) Clustering Based on Grid and Local Density with Priority-Based Expansion for Multi-Density Data. Information Sciences, 468, 103-116. https://doi.org/10.1016/j.ins.2018.08.018
- Abdel-Baset, M., Selim, I.M. and Hezam, I.M. (2015) Cuckoo Search Algorithm for Stellar Population Analysis of Galaxies. International Journal of Information Technology and Computer Science, 7, 29-33. https://doi.org/10.5815/ijitcs.2015.11.04
- Tang, R. and Fong, S. (2018) Clustering Big IoT Data by Metaheuristic Optimized Mini-Batch and Parallel Partition-Based DGC in Hadoop. Future Generation Computer Systems, 86, 1395-1412. https://doi.org/10.1016/j.future.2018.03.006
- Jaros, M., et al. (2017) Implementation of K-Means Segmentation Algorithm on Intel Xeon Phi and GPU: Application in Medical Imaging. Advances in Engineering Software, 103, 21-28. https://doi.org/10.1016/j.advengsoft.2016.05.008