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Multi-channel coronal hole detection with convolutional neural networks  

Robert Jarolim   Submitted: 2021-05-31 02:31

Context. A precise detection of the coronal hole boundary is of primary interest for a better understanding of the physics of coronal holes, their role in the solar cycle evolution, and space weather forecasting. Aims. We develop a reliable, fully automatic method for the detection of coronal holes that provides consistent full-disk segmentation maps over the full solar cycle and can perform in real-time. Methods. We use a convolutional neural network to identify the boundaries of coronal holes from the seven extreme ultraviolet (EUV) channels of the Atmospheric Imaging Assembly (AIA) and from the line-of-sight magnetograms provided by the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO). For our primary model (Coronal Hole RecOgnition Neural Network Over multi-Spectral-data; CHRONNOS) we use a progressively growing network approach that allows for efficient training, provides detailed segmentation maps, and takes into account relations across the full solar disk. Results. We provide a thorough evaluation for performance, reliability, and consistency by comparing the model results to an independent manually curated test set. Our model shows good agreement to the manual labels with an intersection-over-union (IoU) of 0.63. From the total of 261 coronal holes with an area > 1.5e10 km2 identified during the time-period from November 2010 to December 2016, 98.1% were correctly detected by our model. The evaluation over almost the full solar cycle no. 24 shows that our model provides reliable coronal hole detections independent of the level of solar activity. From a direct comparison over short timescales of days to weeks, we find that our model exceeds human performance in terms of consistency and reliability. In addition, we train our model to identify coronal holes from each channel separately and show that the neural network provides the best performance with the combined channel information, but that coronal hole segmentation maps can also be obtained from line-of-sight magnetograms alone. Conclusions. The proposed neural network provides a reliable data set for the study of solar-cycle dependencies and coronal-hole parameters. Given the fast and robust coronal hole segmentation, the algorithm is also highly suitable for real-time space weather applications.

Authors: R. Jarolim, A.M. Veronig, S. Hofmeister, S.G. Heinemann, M. Temmer, T. Podladchikova, K. Dissauer
Projects: SDO-AIA

Publication Status: accepted
Last Modified: 2021-05-31 11:55
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Image Quality Assessment for Full-Disk Solar Observations with Generative Adversarial Networks  

Robert Jarolim   Submitted: 2020-08-28 01:05

Within the last decades, solar physics has entered the era of big data and the amount of data being constantly produced from ground- and space-based observatories can no longer be purely analyzed by human observers. In order to assure a stable series of recorded images of sufficient quality for further scientific analysis, an objective image quality measure is required. Especially when dealing with ground-based observations, which are subject to varying seeing conditions and clouds, the quality assessment has to take multiple effects into account and provide information about the affected regions. The automatic and robust identification of quality-degrading effects is a critical task, in order to maximize the scientific return from the observations and to allow for event detections in real-time. In this study, we develop a deep learning method that is suited to identify anomalies and provide an image quality assessment of solar full-disk Hα filtergrams. The approach is based on the structural appearance and the true image distribution of high-quality observations. We employ a neural network with an encoder-decoder architecture to perform an identity transformation of selected high-quality observations. The encoder network is used to achieve a compressed representation of the input data, which is reconstructed to the original by the decoder. We use adversarial training to recover truncated information based on the high-quality image distribution. When images with reduced quality are transformed, the reconstruction of unknown features (e.g., clouds, contrails, partial occultation) shows deviations from the original. This difference is used to quantify the quality of the observations and to identify the affected regions. In addition, we present an extension of this architecture by using also low-quality samples in the training step, which takes the characteristics of both quality domains into account and improves the sensitivity for minor image quality degradation. We apply our method to full-disk Hα filtergrams from Kanzelhöhe Observatory recorded during 2012-2019 and demonstrate its capability to perform a reliable image quality assessment for various atmospheric conditions and instrumental effects. Our quality metric achieves an accuracy of 98.5% in distinguishing observations with quality-degrading effects from clear observations and provides a continuous quality measure which is in good agreement with the human perception. The developed method is capable of providing a reliable image quality assessment in real-time, without the requirement of reference observations. Our approach has the potential for further application to similar astrophysical observations and requires only little effort of manual labeling.

Authors: R. Jarolim, A. M. Veronig, W. Pötzi, T. Podladchikova
Projects: None

Publication Status: accepted
Last Modified: 2020-09-01 16:40
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Multi-channel coronal hole detection with convolutional neural networks
Image Quality Assessment for Full-Disk Solar Observations with Generative Adversarial Networks

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