Type: Talk
Session: Galaxy Morphology and Low Surface Brightness Features
Author: Aleksandr Mosenkov

Abstract: Automated identification of optical cirrus is extremely important for processing and studying data from deep sky surveys. Optical cirrus can significantly affect the results of studying extragalactic low surface brightness objects: it can lead to the detection of false objects, background subtraction errors, and distortions of the properties of objects under study. We have developed an effective algorithm for detecting faint cirrus clouds and filaments in deep sky surveys, employing specially designed neural networks. Using our algorithm, we created a map of cirrus in the SDSS Stripe 82 and HSC SSP deep sky surveys. We measured cirrus optical colors, average surface brightnesses, and the general geometric characteristics of their spatial distribution. The created cirrus maps can be used in studies of extragalactic sources and in planning observations. In addition, the obtained characteristics of cirrus are important for studying the interstellar medium in our Galaxy as a whole. The results of this work are of particular importance for identifying and exploring low surface brightness features around and within galaxies, groups, and clusters of galaxies that will be imaged by the Vera C. Rubin Observatory.