The Evolution of Coronal Holes over Three Solar Cycles Using the McIntosh Archive

Hewins, Ian; Gibson, S. E.; Webb, D. F.; McFadden, Robert; Kuchar, T. A.; Emery, B. A.; McIntosh, Scott W.

doi:10.1007/s11207-020-01731-y

Cited by 25 publications

(40 citation statements)

References 34 publications

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“…The drift of these trails in longitude with time is due to their rotation rate being slightly slower than the rotation rate used to define Carrington longitude. These trails agree with statistical results on the lifetimes of equatorial coronal holes, showing that they can exist for up to three years (Hewins et al, 2020).…”

Section: Location Of Open Field Footpointssupporting

confidence: 89%

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Active Region Contributions to the Solar Wind over Multiple Solar Cycles

et al. 2021

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Both coronal holes and active regions are source regions of the solar wind. The distribution of these coronal structures across both space and time is well known, but it is unclear how much each source contributes to the solar wind. In this study we use photospheric magnetic field maps observed over the past four solar cycles to estimate what fraction of magnetic open solar flux is rooted in active regions, a proxy for the fraction of all solar wind originating in active regions. We find that the fractional contribution of active regions to the solar wind varies between 30% to 80% at any one time during solar maximum and is negligible at solar minimum, showing a strong correlation with sunspot number. While active regions are typically confined to latitudes ±30∘ in the corona, the solar wind they produce can reach latitudes up to ±60∘. Their fractional contribution to the solar wind also correlates with coronal mass ejection rate, and is highly variable, changing by ±20% on monthly timescales within individual solar maxima. We speculate that these variations could be driven by coronal mass ejections causing reconfigurations of the coronal magnetic field on sub-monthly timescales.

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Section: Location Of Open Field Footpointssupporting

confidence: 89%

“…Further work combining our methodology with EUV observations of coronal holes (e.g. Hess Webber et al, 2014;Hewins et al, 2020) to constrain R ss could be used to remove this limitation.…”

Section: Discussionmentioning

confidence: 99%

Active Region Contributions to the Solar Wind over Multiple Solar Cycles

et al. 2021

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“…The precise magnetic topology of coronal holes is an active topic of research, where methods such as the boundary detection from EUV images or extrapolation of photospheric magnetic fields can only provide a fraction of the information and the true coronal hole boundary still remains elusive. For spaceweather aspects and the statistical study of coronal holes, the estimate of the boundary is still a key component (Rotter et al 2012;Hofmeister et al 2019;Asvestari et al 2019;Hewins et al 2020). As the definition of the boundary strongly depends on the detection method (see Reiss et al (2021) for a comparison of nine different methods for CH detection) and the wavelength range used, we argue that the consistency of the coronal hole detection is decisive for further applications.…”

Section: Discussionmentioning

confidence: 83%

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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“…Only 1% for CHRONNOS and a maximum of 6% for SCAN-magnetogram lie in the range be- The precise magnetic topology of coronal holes is an active topic of research, where methods such as the boundary detection from EUV images or extrapolation of photospheric magnetic fields can only provide a fraction of the information and the true coronal hole boundary still remains elusive. For spaceweather aspects and the statistical study of coronal holes, the estimate of the boundary is still a key component (Rotter et al 2012;Hofmeister et al 2019;Asvestari et al 2019;Hewins et al 2020). Since the definition of the boundary strongly depends on the detection method (see Reiss et al (2021) for a comparison of nine different methods for CH detection) and the wavelength range used, we argue that the consistency of the coronal hole detection is decisive for further applications.…”

Section: Discussionmentioning

confidence: 89%

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 Evolution of Coronal Holes over Three Solar Cycles Using the McIntosh Archive

Cited by 25 publications

References 34 publications

Active Region Contributions to the Solar Wind over Multiple Solar Cycles

Active Region Contributions to the Solar Wind over Multiple Solar Cycles

Multi-channel coronal hole detection with convolutional neural networks

Multi-Channel Coronal Hole Detection with Convolutional Neural Networks

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