Astrometric Calibration for All-sky Camera with Machine Learning. (1st March 2022)

• Record Type: Journal Article
• Authors: Tian, J. F.; Ge, L.; Wu, Y.; Zhou, Z. Z.
• Is Part Of: Publications of the Astronomical Society of the Pacific. Volume 134:Number 1033(2022)
• Publication Date: 2022-03-01
• Subjects: Neural networks -- All-sky cameras -- Calibration; Astronomy -- Periodicals; Astronomy; Periodicals; Periodicals; 520.5
• DOI: 10.1088/1538-3873/ac5316
• Languages: English
• ISSNs: 0004-6280

Abstract

Abstract: The night images obtained with an all-sky camera can provide spatial and time sampling, which can be used for measurement cloud coverage measurement and meteor monitoring. The astrometric calibration of an all-sky camera is necessary because of strong field distortions. We use machine learning to complete the calibration of an all-sky camera. In order to prepare the data sets needed for machine learning, a particle swarm optimization algorithm is used to determine the parameters of the method proposed by Borovicka in 1995. Machine learning can transform plate coordinates to celestial coordinates and transform celestial coordinates to plate coordinates. The actual test shows that the standard deviation of residuals is of the order of 1′ for the transformation from plate coordinates to celestial coordinates and the standard deviation of residuals is of the order of 0.3 px for the transformation from celestial coordinates to the plate coordinates.