Machine learning based stellar classification with highly sparse photometry data.

Background: Identifying stars belonging to different classes is vital in order to build up statistical samples of different phases and pathways of stellar evolution. In the era of surveys covering billions of stars, an automated method of identifying these classes becomes necessary.

Methods: Many classes of stars are identified based on their emitted spectra.