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A Comparative Evaluation of Automated Solar Filament Detection |
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| Authors: | M A Schuh J M Banda P N Bernasconi R A Angryk P C H Martens |
| Institution: | 1. Department of Computer Science, Montana State University, 357 EPS, Bozeman, MT, 59717-3880, USA 2. Applied Physics Laboratory, Johns Hopkins University, 11100 Johns Hopkins Road, Laurel, MD, 20723, USA 3. Department of Physics, Montana State University, 247 EPS, Bozeman, MT, 59717-3880, USA 4. Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA, 02138, USA |
| Abstract: | We present a comparative evaluation for automated filament detection in Hα solar images. By using metadata produced by the Advanced Automated Filament Detection and Characterization Code (AAFDCC) module, we adapted our trainable feature recognition (TFR) module to accurately detect regions in solar images containing filaments. We first analyze the AAFDCC module’s metadata and then transform it into labeled datasets for machine-learning classification. Visualizations of data transformations and classification results are presented and accompanied by statistical findings. Our results confirm the reliable event reporting of the AAFDCC module and establishes our TFR module’s ability to effectively detect solar filaments in Hα solar images. |
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