| Summary: | 碩士 === 國立中央大學 === 資訊工程學系 === 107 === In astronomical researches, the rotation periods of asteroids can be derived from their light curves which are the brightness as a function of time. Traditionally, a periodical analysis is performed on the light curve, and then astronomers determine the category of a possible period according to the folded light curve (i.e., a light curve folded to a particular period). This process was relied on human inspection, but it becomes very formidable due to the advancement in the technology of astronomical observation in the last decade that increases the volume of astronomical data set dramatically. Therefore, manual inspection is no longer feasible, and it is necessary to adopt an automatic method to replace the aforementioned time-consuming human review process.
In this research, we use the asteroid light curves obtained from the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS). The light curves are fitted using the second-order Fourier series to find the possible rotation periods from the periodogram (i.e., the reduced 2 as a function of period). Then, a folded light curve of a certain period is generated. When a folded light curve shows a clear trend with a double-peak feature (i.e., similar to W), it is identified as a full rotation period. If a folded light curve only shows a single peak, a half rotation period is suggested (i.e., similar to V). The other cases are seen as no period found. Therefore, we deployed a deep learning technology, using convolutional neural network (CNN) as a network architecture, to construct a model to classify the folded light curves to W, V, and other shapes. From the study, we found that our model can precisely and yet much more effectively recognize a result consistent with that of human inspection.
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