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Automated classification technique for edge-on galaxies based on mathematical treatment of brightness data

Research in Astronomy and Astrophysics 2021年第10期21卷 231-238页

作者： Mohamed Eassa Ibrahim Mohamed Selim Walid Dabour passent elkafrawy Computer Science Department Integrated Thebes InstituteCairoEgypt Faculty of Computer&Artificial Intelligence Sadat City UniversitySadat CityEgypt Math and Computer Science Dept. Faculty of ScienceMenoufia UniversityEgypt Computer Science Dept. Taibah UniversityAlula BranchKingdom of Saudi Arabia School of Information Technology and Computer Science(ITCS) Nile UniversityEgypt

Classification of edge-on galaxies is important to astronomical studies due to our Milky Way galaxy being an edge-on ***-on galaxies pose a problem to classification due to their less overall brightness levels and smaller numbers of *** the current work,a novel technique for the classification of edge-on galaxies has been *** technique is based on the mathematical treatment of galaxy brightness data from their images.A special treatment for galaxies’brightness data is developed to enhance faint galaxies and eliminate adverse effects of high brightness backgrounds as well as adverse effects of background bright stars.A novel slimness weighting factor is developed to classify edge-on galaxies based on their *** technique has the capacity to be optimized for different catalogs with different brightness *** the current work,the developed technique is optimized for the EFIGI catalog and is trained using a set of 1800 galaxies from this *** classification of the full set of 4458 galaxies from the EFIGI catalog,an accuracy of 97.5% has been achieved,with an average processing time of about 0.26 seconds per galaxy on an average laptop.

关键词： techniques:image processing methods:data analysis galaxies:formation

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Virgo Cluster Membership Based on <i>K</i>-Means Algorithm

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International Journal of Astronomy and Astrophysics 2020年第1期10卷 1-10页

作者： Ibrahim Mohamed Selim passent elkafrawy Walid Dabour Mohamed Eassa National Research Institute of Astronomy and Geophysics Cairo Egypt Computer Science Department Integrated Thebes Institute Cairo Egypt Math and Computer Science Department Faculty of Science Menoufia University Shibin El Kom Egypt Computer Science Department Taibah University Alula Branch Medina KSA

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.

关键词： Virgo Cluster K-Means Normal Distribution

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