Astronomical objects classification based on the Digitized First Byurakan Survey low-dispersion spectra

Abstract The Digitized First Byurakan Survey is the largest and the first systematic objective-prism survey of the extragalactic sky. The detection, extraction, and classification of about 40 million spectra of about 20 million astronomical objects available in the survey require distinguishing the pixels containing photons from the source and the noise pixels per object. This paper aims at developing a service to classify the spectra of UV-excess galaxies, quasars, compact galaxies, and other objects in the survey. Supervised and unsupervised convolutional neural network deep learning algorithms have been developed and studied.

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