Solar Coronal Magnetic Field Extrapolation from Synchronic Data with AI-generated Farside

Jeong, Hyun-Jin; Moon, Yong‐Jae; Park, Eunsu; Lee, Harim

doi:10.3847/2041-8213/abc255

Cited by 36 publications

(36 citation statements)

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“…The direct detection of coronal holes from LOS magnetograms is particularly interesting for ground-based observations, where coronal imaging is not possible, but magnetograms can be obtained on a regular basis (Harvey et al 1996). Magnetograms are used for coronal hole detection with the use of magnetic field extrapolation (Pomoell & Poedts 2018;Pizzo et al 2011;Asvestari et al 2019;Jeong et al 2020). Here we use a different approach, by directly estimating the coronal hole boundary from photospheric magnetograms.…”

Section: Channel Importance (Scan)mentioning

confidence: 99%

“…Solar coronal holes are usually identified from (1) EUV or X-ray images reflecting the density distribution of highly ionized ions in the solar corona (Vršnak et al 2007;Rotter et al 2012;Krista & Gallagher 2009;Verbeeck et al 2014;Lowder et al 2014;Garton et al 2018;Illarionov & Tlatov 2018;Hamada et al 2018), (2) He I 10 830 Å images probing the solar chromosphere (Henney & Harvey 2005;Tlatov et al 2014;Webb et al 2018), or (3) magnetic field extrapolations (Pomoell & Poedts 2018;Pizzo et al 2011;Asvestari et al 2019;Jeong et al 2020). In EUV and soft X-ray images, coronal holes appear as distinct dark features because of their reduced density and temperature.…”

Section: Introductionmentioning

confidence: 99%

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Multi-channel coronal hole detection with convolutional neural networks

Jarolim

Veronig

Hofmeister

et al. 2021

A&A

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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 > 1.5 • 10 10 km 2 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.

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Section: Channel Importance (Scan)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-channel coronal hole detection with convolutional neural networks

Jarolim

Veronig

Hofmeister

et al. 2021

A&A

View full text Add to dashboard Cite

show abstract

“…The direct detection of coronal holes from LOS magnetograms is particularly interesting for ground-based observations, that can not account for coronal imaging, but provide magnetograms on a regular basis (Harvey et al 1996). Magnetograms are used for coronal hole detection with the use of mag- netic field extrapolation (Pomoell & Poedts 2018;Pizzo et al 2011;Asvestari et al 2019;Jeong et al 2020). Here we use a different approach, by directly estimating the coronal hole boundary from photospheric magnetograms.…”

Section: Channel Importance (Scan)mentioning

confidence: 99%

“…Solar coronal holes are usually either identified from (1) EUV or X-ray images reflecting the density distribution of highly ionized ions in the solar corona (Vršnak et al 2007;Rotter et al 2012;Krista & Gallagher 2009;Verbeeck et al 2014;Lowder et al 2014;Garton et al 2018;Illarionov & Tlatov 2018;Hamada et al 2018), (2) He I 10 830 Å images probing the solar chromosphere (Henney & Harvey 2005;Tlatov et al 2014;Webb et al 2018), or (3) magnetic field extrapolations (Pomoell & Poedts 2018;Pizzo et al 2011;Asvestari et al 2019;Jeong et al 2020). In EUV and soft X-ray images, coronal holes appear as distinct dark features due to their reduced density and temperature.…”

Section: Introductionmentioning

confidence: 99%

Multi-Channel Coronal Hole Detection with Convolutional Neural Networks

Jarolim

Veronig

Hofmeister

et al. 2021

Preprint

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<p>Being the source region of fast solar wind streams, coronal holes are one of the key components which impact space weather. The precise detection of the coronal hole boundary is an important criterion for forecasting and solar wind modeling, but also challenges our current understanding of the magnetic structure of the Sun. We use deep-learning to provide new methods for the detection of coronal holes, based on the multi-band EUV filtergrams and LOS magnetogram from the AIA and HMI instruments onboard the Solar Dynamics Observatory. The proposed neural network is capable to simultaneously identify full-disk correlations as well as small-scale structures and efficiently combines the multi-channel information into a single detection. From the comparison with an independent manually curated test set, the model provides a more stable extraction of coronal holes than the samples considered for training. Our method operates in real-time and provides reliable coronal hole extractions throughout the solar cycle, without any additional adjustments. We further investigate the importance of the individual channels and show that our neural network can identify coronal holes solely from magnetic field data.</p>

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“…The total data size from SDO to date is approximately 5 PB. This big data set obtained by SDO enables various studies and the application of deep learning techniques in solar physics (Armstrong and Fletcher, 2019;Jeong et al, 2020;Kim et al, 2019;Kucuk, Aydin, and Angryk, 2017;Park et al, 2018Park et al, , 2020Rahman et al, 2020). We use SDO/AIA and HMI data to train the solar event auto-detection models.…”

Section: Solar Dynamics Observatory (Sdo)mentioning

confidence: 99%

Solar Event Detection Using Deep-Learning-Based Object Detection Methods

et al. 2021

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Research on the detection of solar events has been conducted over many years. Recently, deep learning and data-driven approaches have been applied to solar event recognition. In this study, we present solar event detection using deep-learning-based object detection methods for real-time space weather monitoring. First, we construct a new object detection dataset using imaging data obtained by the Solar Dynamics Observatory with bounding boxes as labels for three representative features: coronal holes, sunspots, and prominences. Second, we train two representative object detection models: the Single Shot MultiBox Detector (SSD) and the Faster Region-based Convolutional Neural Network (R-CNN) using the new dataset. The results show that both models perform similarly well for coronal hole and sunspot detection. For prominence detection, the SSD and Faster R-CNN exhibited relatively low performance. This study demonstrates that deep-learning-based object detection can successfully detect multiple types of solar events, and it may be extended to detect other solar events. In addition, we provide the dataset for further achievements of object detection studies in solar physics.

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Solar Coronal Magnetic Field Extrapolation from Synchronic Data with AI-generated Farside

Cited by 36 publications

References 50 publications

Multi-channel coronal hole detection with convolutional neural networks

Multi-channel coronal hole detection with convolutional neural networks

Multi-Channel Coronal Hole Detection with Convolutional Neural Networks

Solar Event Detection Using Deep-Learning-Based Object Detection Methods

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