On the Estimation of the SHARP Parameter MEANALP from AIA Images Using Deep Neural Networks
Abstract
Space-weather HMI Active Region Patches (SHARPs) data from the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO) provides high cadence data from the full-disk photospheric magnetic field. The SHARP's MEANALP (αm) parameter, which characterizes the twist, can provide a measure of nonpotentiality of an active region, which can be a condition for the occurrence of solar flares. The SDO/Atmospheric Imaging Assembly (AIA) captures images at a higher cadence (12 or 24 seconds) than the SDO/HMI. Hence, if the αm can be inferred from the AIA data, we can estimate the magnetic field evolution of an active region at a higher temporal cadence. Shortly before a flare occurs, we observed a change in the αm in some active regions that produced stronger (M- or X-class) flares. Therefore, we study the ability of neural networks to estimate the αm parameter from SDO/AIA images. We propose a classification and regression scheme to train deep neural networks using AIA filtergrams of active regions with the objective to estimate the αm of active regions outside our training set. Our results show a classification accuracy greater than 85% within two classes to identify the range of the αm parameter. We also attempt to understand the nature of the solar images using variational autoencoders. Thus, this study opens a promising new application of neural networks which can be extended to other SHARP parameters in the future.
- Publication:
-
Solar Physics
- Pub Date:
- November 2021
- DOI:
- 10.1007/s11207-021-01912-3
- Bibcode:
- 2021SoPh..296..163B
- Keywords:
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- Deep neural networks;
- Active regions;
- SHARP data;
- SHARP MEANALP