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Automated Detection Methods for Solar Activities and an Application for Statistic Analysis of Solar Filament

Published online by Cambridge University Press:  27 November 2018

Q. Hao Open the ORCID record for Q. Hao [Opens in a new window] ,
P. F. Chen  and
C. Fang
Q. Hao
Affiliation:
School of Astronomy and Space Science, Nanjing University, Nanjing 210023, China email: [email protected] Key Laboratory of Modern Astronomy and Astrophysics (Nanjing University), Ministry of Education, China
P. F. Chen
Affiliation:
School of Astronomy and Space Science, Nanjing University, Nanjing 210023, China email: [email protected] Key Laboratory of Modern Astronomy and Astrophysics (Nanjing University), Ministry of Education, China
C. Fang
Affiliation:
School of Astronomy and Space Science, Nanjing University, Nanjing 210023, China email: [email protected] Key Laboratory of Modern Astronomy and Astrophysics (Nanjing University), Ministry of Education, China
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Abstract

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With the rapid development of telescopes, both temporal cadence and the spatial resolution of observations are increasing. This in turn generates vast amount of data, which can be efficiently searched only with automated detections in order to derive the features of interest in the observations. A number of automated detection methods and algorithms have been developed for solar activities, based on the image processing and machine learning techniques. In this paper, after briefly reviewing some automated detection methods, we describe our efficient and versatile automated detection method for solar filaments. It is able not only to recognize filaments, determine the features such as the position, area, spine, and other relevant parameters, but also to trace the daily evolution of the filaments. It is applied to process the full disk Hα data observed in nearly three solar cycles, and some statistic results are presented.

Keywords

Sun: activitymethods: data analysismethods: statisticaltechniques: image processing
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 13 , Symposium S340: Long-term Datasets for the Understanding of Solar and Stellar Magnetic Cycles , February 2018 , pp. 101 - 107
Copyright
Copyright © International Astronomical Union 2018 

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