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

Classification of edge-on galaxies is important to astronomical studies due to our Milky Way galaxy being an edge-on galaxy.Edge-on galaxies pose a problem to classification due to their less overall brightness levels and smaller numbers of pixels.In the current work,a novel technique for the classification of edge-on galaxies has been developed.This 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 slimness.The technique has the capacity to be optimized for different catalogs with different brightness levels.In the current work,the developed technique is optimized for the EFIGI catalog and is trained using a set of 1800 galaxies from this catalog.Upon 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.

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

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.