GalaDC: Galaxy Detection and Classification Tool

Image is one of the core concerns in modern Astronomy. Telescopes capture photons emitted from sources deep inside the universe, forming images or spectrums which then be analyzed by astronomers. In the recent decades, people have built large amount of land-based and space-based telescopes which are observing light covering a wide range of wave length. The amount of the imaging data increased rapidly. For a typical integral field unit (also called the IFU) telescope, 60 GB of data is generated each night. The requirements of real time processing of these data raised challenges to astronomers. These requirements necessitate the developing of efficient computer algorithms. One important part of these requirements is the classification of galaxies. The morphologies of the galaxies can contribute in many aspects of the astronomical studies. The distribution of galaxies of different morphologies (for example ecliptic and spiral) can reflect certain large scale characteristic of the universe, such as the evolution of the galaxies, and the distribution of Hydrogen in the universe. In this work, we train a neural network and use a series of computer vision algorithms to build a Galaxy Detection and Classification Tool (GalaDC), which can detect and classify galaxies with high efficiency and accuracy. GalaDC is user friendly, supports batch processing, and is suitable for handling images which consists of multiple galaxies and do statistical analysis.