Automatic photometric processing methods for star variability identification
Sergey Bratarchuk1, Zlata Potiļicina2
1RTU MTAF AERTI, Lomonosova Str, 1v, Riga, LV-1003, Latvia
COMPUTER MODELLING & NEW TECHNOLOGIES 2019 23(1) 22-28
In the task of variable star detection exists a problem of missing data. By using shared telescope networks like LCO, users often face the concurrence for the observation time. This concurrence does not let to make a lot of photos of the same part of the sky. The author of the research proposes a new method for the solution of the missing data or unevenly based data problem in the task of variable stars’ detection. Method is based on the addition of your own variable star data by using the data of other researchers. Author suggests an algorithm that identifies the star of interest on the series of photos. Algorithm automatically identifies the stars on the different images independently from the shift or rotation of the stars on the image. Then the algorithm extracts the data about the flux and magnitude of the stars on the image. In this way, by getting data about the magnitude and flux of the star from different sources, it is possible to fill the gaps in data that will increase the probability that a star will be identified as a variable one.