Deep Learning-based Detection and Segmentation of Edge-on and Highly Inclined Galaxies

Abstract Edge-on galaxies have many important applications in galactic astrophysics, but they can be difficult to identify in vast amounts of astronomical data. To facilitate the search for them, we developed a deep learning algorithm designed to identify and extract edge-on galaxies from astronomical images. We utilized a sample of edge-on spiral galaxies from the Galaxy Zoo database, retrieving the corresponding images from the Sloan Digital Sky Survey (SDSS). Our data set comprised ∼16,000 galaxies, which we used to train the YOLOv5 algorithm for detection purposes. To isolate galaxies from their backgrounds, we trained the SCSS-Net neural network to generate segmentation masks. As a result, our algorithm detected ∼12,000 edge-on galaxies with a high confidence, for which we compiled a catalog including their parameters obtained from the SDSS database. We described basic properties of our sample, finding that most galaxies have redshifts 0.02 < z < 0.10, have low values of b / a and are mostly red, which is expected from edge-on galaxies and is consistent with our training sample, as well as other literature. The cutouts of the detected galaxies can be used for future studies and the algorithm can be applied to data from future surveys as well.