Mirosław Kicia, Mikołaj Kałuszyński, Marek Górski, Rolf Chini, Grzegorz Pietrzyński
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A machine learning method for predicting telescope cycle time applied to the Cerro Murphy Observatory
Telescope cycle time estimation is one of the basic issues of observational astronomy. There are not many tools that help to calulate the cycle time for multiple telescopes with multiple instruments. This work presents a new tool for determing the observation time; it was applied at the Cerro Murphy Observatory (OCM) but can be used at any other observatory. The Machine Learning (ML) method was implied, resulting in a fully automatic software module that works without any user intervention. We propose a polynomial multiple regression method and demonstrate all steps to build a reliable ML model like data collecting, data cleaning, model training and error evaluation in relation to the implementation in the observatory software. The method was designed to work for different telescopes with several instruments. Accuracy analysis and the assessment of model errors were based on real data from telescopes, proving the usefulness of the presented method. Error evaluation shows that for 84.2 % of nights, the prediction error in operation time prediction does not exceed 2 %. Converted into a 10-hour observation night, 2 % corresponds to an error of no more than 12 minutes. The described model is already working at the OCM and optimizes the efficiency of the observations.
期刊介绍:
Many new instruments for observing astronomical objects at a variety of wavelengths have been and are continually being developed. Furthermore, a vast amount of effort is being put into the development of new techniques for data analysis in order to cope with great streams of data collected by these instruments.
Experimental Astronomy acts as a medium for the publication of papers of contemporary scientific interest on astrophysical instrumentation and methods necessary for the conduct of astronomy at all wavelength fields.
Experimental Astronomy publishes full-length articles, research letters and reviews on developments in detection techniques, instruments, and data analysis and image processing techniques. Occasional special issues are published, giving an in-depth presentation of the instrumentation and/or analysis connected with specific projects, such as satellite experiments or ground-based telescopes, or of specialized techniques.