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On 2024 May 10/11, the strongest geomagnetic storm since November 2003 has occurred, with a peak Dst index of -412 nT. The storm was caused by NOAA Active Region (AR) 13664, which was the source of a large number of coronal mass ejections and flares, including 12 X-class flares. Starting from about May 7, AR 13664 showed a steep increase in its size and (free) magnetic energy, along with increased flare activity. In this study, we perform 3D magnetic field extrapolations with the NF2 nonlinear-force free code based on physics informed neural networks (Jarolim et al. 2023). In addition, we introduce the computation of the vector potential to achieve divergence-free solutions. We extrapolate vector magnetograms from SDO/HMI at the full 12 minute cadence from 2024 May 5-00:00 to 11-04:36 UT, in order to understand the active regions magnetic evolution and the large eruptions it produced. The computed change in magnetic energy and free magnetic energy shows a clear correspondence to the flaring activity. Regions of free magnetic energy and depleted magnetic energy indicate the flare origin and are in good correspondence with observations in Extreme Ultraviolet. Our results suggest that the modeled solar flares are related to significant topological reconfigurations. We provide a detailed analysis of the X4.0-class flare on May 10, where we show that the interaction between separated magnetic domains is directly linked to major flaring events. With this study, we provide a comprehensive data set of the magnetic evolution of AR 13664 and make it publicly available for further analysis.

We studied the rapid filament evolution in AR 12975 during a confined C2 flare on 28 March 2022, which led to an eruptive M4 flare 1.5 h later. It is characterized by the breakup of the filament, the disappearance of its southern half, and the flow of the remaining plasma into a longer channel with a topology similar to an EUV hot channel during the flare. Our multipoint study takes advantage of Solar Orbiter's position at 0.33 AU and 83. 5{\deg} west of the Sun-Earth line. STIX and EUI onboard Solar Orbiter observed the event at the limb. AIA and HMI onboard SDO provided on-disk observations from which we derived DEM maps and NLFF magnetic field extrapolations. We find that both filament channels likely existed in close proximity before the flare. Based on field structures associated with AIA 1600 {\AA} flare ribbons and kernels, we propose a loop-loop reconnection scenario between field lines that surround and pass beneath the shorter filament channel, and field lines following a portion of the longer channel. Reconnection occurs in an essentially vertical current sheet at a PIL below the breakup region, leading to the formation of the flare loop arcade and the EUV hot channel. The scenario is supported by concentrated currents and free magnetic energy built up by antiparallel flows along the PIL before the flare. The reconnection probably propagated to involve the original filament itself, leading to its breakup and reformation. The reconnection geometry provides a general mechanism for the formation of the long filament channel and realizes the concept of tether cutting, which was active throughout the filament's rise phase, lasting from at least 30 min before the C2 flare until the eruption. The C2 flare represents a period of fast reconnection during this otherwise more steady process, during which most of the original filament was reconnected and joined the longer channel.

Filaments are omnipresent features in the solar atmosphere. Their location, properties and time evolution can provide information about changes in solar activity and assist the operational space weather forecast. Therefore, filaments have to be identified in full disk images and their properties extracted from these images. Manual extraction is tedious and takes much time; extraction with morphological image processing tools produces a large number of false-positive detections. Automatic object detection, segmentation, and extraction in a reliable manner allows to process more data in a shorter time. The Chromospheric Telescope (ChroTel), Tenerife, Spain, the Global Oscillation Network Group (GONG), and the Kanzelh\"ohe Observatory (KSO), Austria, provide regular full-disk observations of the Sun in the core of the chromospheric H-alpha absorption line. We present a deep learning method that provides reliable extractions of filaments from H-alpha filtergrams. First, we train the object detection algorithm YOLOv5 with labeled filament data of ChroTel. We use the trained model to obtain bounding-boxes from the full GONG archive. In a second step, we apply a semi-supervised training approach, where we use the bounding boxes of filaments, to learn a pixel-wise classification of filaments with u-net. Here, we make use of the increased data set size to avoid overfitting of spurious artifacts from the generated training masks. Filaments are predicted with an accuracy of 92%. With the resulting filament segmentations, physical parameters such as the area or tilt angle can be easily determined and studied. This we demonstrate in one example, where we determine the rush-to-the pole for Solar Cycle 24 from the segmented GONG images. In a last step, we apply the filament detection to H-alpha observations from KSO which demonstrates the general applicability of our method to H-alpha filtergrams., Comment: 15 pages, 14 figures

To understand its evolution and the effects of its eruptive events, the Sun is permanently monitored by multiple satellite missions. The optically-thin emission of the solar plasma and the limited number of viewpoints make it challenging to reconstruct the geometry and structure of the solar atmosphere; however, this information is the missing link to understand the Sun as it is: a three-dimensional evolving star. We present a method that enables a complete 3D representation of the uppermost solar layer (corona) observed in extreme ultraviolet (EUV) light. We use a deep learning approach for 3D scene representation that accounts for radiative transfer, to map the entire solar atmosphere from three simultaneous observations. We demonstrate that our approach provides unprecedented reconstructions of the solar poles, and directly enables height estimates of coronal structures, solar filaments, coronal hole profiles, and coronal mass ejections. We validate the approach using model-generated synthetic EUV images, finding that our method accurately captures the 3D geometry of the Sun even from a limited number of 32 ecliptic viewpoints ($|\text{latitude}| \leq 7^\circ$). We quantify uncertainties of our model using an ensemble approach that allows us to estimate the model performance in absence of a ground-truth. Our method enables a novel view of our closest star, and is a breakthrough technology for the efficient use of multi-instrument datasets, which paves the way for future cluster missions.

The cause of excess spectral line broadening (non-thermal velocity) is not definitively known, but given its rise before and during flaring, the causal processes hold clues to understanding the triggers for the onset of reconnection and the release of free magnetic energy from the coronal magnetic field. A comparison of data during a 9-hour period from the extreme ultraviolet (EUV) Imaging Spectrometer (EIS) on the Hinode spacecraft - at a 3-minute cadence - and non-linear force-free field (NLFFF) extrapolations performed on Helioseismic and Magnetic Imager (HMI) magnetograms - at a 12-minute cadence - shows an inverse relationship between non-thermal velocity and free magnetic energy on short timescales during two X-class solar flares on 6 September 2017. Analysis of these results supports suggestions that unresolved Doppler flows do not solely cause non-thermal broadening and instead other mechanisms like Alfv\'en wave propagation and isotropic turbulence have a greater influence., Comment: Accepted for publication in ApJL

Non-linear force-free extrapolations are a common approach to estimate the 3D topology of coronal magnetic fields based on photospheric vector magnetograms. The force-free assumption is a valid approximation at coronal heights, but for the dense plasma conditions in the lower atmosphere, this assumption is not satisfied. In this study, we utilize multi-height magnetic field measurements in combination with physics-informed neural networks to advance solar magnetic field extrapolations. We include a flexible height-mapping, which allows us to account for the different formation heights of the observed magnetic field measurements. The comparison to analytical and simulated magnetic fields demonstrates that including chromospheric magnetic field measurements leads to a significant improvement of our magnetic field extrapolations. We also apply our method to chromospheric line-of-sight magnetograms, from the Vector Spectromagnetograph (VSM) on the Synoptic Optical Long-term Investigations of the Sun (SOLIS) observatory, in combination with photospheric vector magnetograms, from the Helioseismic Magnetic Imager (HMI) onboard the Solar Dynamic Observatory (SDO). The comparison to observations in extreme ultraviolet wavelengths shows that the additional chromospheric information leads to a better agreement with the observed coronal structures. In addition, our method intrinsically provides an estimate of the corrugation of the observed magnetograms. With this new approach, we make efficient use of multi-height magnetic field measurements and advance the realism of coronal magnetic field simulations.

We present a case study of an M4-class flare on 28 March 2022, near Solar Orbiter's first science perihelion (0.33 AU). Solar Orbiter was 83.5{\deg} west of the Sun-Earth line, making the event appear near the eastern limb, while Earth-orbiting spacecraft observed it near the disk center. The timing and location of the STIX X-ray sources were related to the plasma evolution observed in the EUV by the Extreme Ultraviolet Imager (EUI) on Solar Orbiter and the Atmospheric Imaging Assembly (AIA) on the Solar Dynamics Observatory, and to the chromospheric response observed in 1600 {\AA} by AIA. We performed differential emission measure (DEM) analysis to further characterize the flaring plasma at different subvolumes. The pre-flare magnetic field configuration was analyzed using a nonlinear force-free (NLFF) extrapolation. In addition to the two classical hard X-ray (HXR) footpoints at the ends of the flaring loops, later in the event we observe a nonthermal HXR source at one of the anchor points of the erupting filament. The evolution of the AIA 1600~{\AA} footpoints indicates that this change in footpoint location represents a discontinuity in an otherwise continuous westward motion of the footpoints throughout the flare. The NLFF extrapolation suggests that strongly sheared field lines close to, or possibly even part of, the erupting filament reconnected with a weakly sheared arcade during the first HXR peak. The remainder of these field lines reconnected later in the event, producing the HXR peak at the southern filament footpoint. Our results show that the reconnection between field lines with very different shear in the early phase of the flare plays a crucial role in understanding the motion of the HXR footpoint during later parts of the flare. This generalizes simpler models, such as whipping reconnection, which only consider reconnection propagating along uniformly sheared arcades.

Extreme Ultraviolet (EUV) light emitted by the Sun impacts satellite operations and communications and affects the habitability of planets. Currently, EUV-observing instruments are constrained to viewing the Sun from its equator (i.e., ecliptic), limiting our ability to forecast EUV emission for other viewpoints (e.g. solar poles), and to generalize our knowledge of the Sun-Earth system to other host stars. In this work, we adapt Neural Radiance Fields (NeRFs) to the physical properties of the Sun and demonstrate that non-ecliptic viewpoints could be reconstructed from observations limited to the solar ecliptic. To validate our approach, we train on simulations of solar EUV emission that provide a ground truth for all viewpoints. Our model accurately reconstructs the simulated 3D structure of the Sun, achieving a peak signal-to-noise ratio of 43.3 dB and a mean absolute relative error of 0.3\% for non-ecliptic viewpoints. Our method provides a consistent 3D reconstruction of the Sun from a limited number of viewpoints, thus highlighting the potential to create a virtual instrument for satellite observations of the Sun. Its extension to real observations will provide the missing link to compare the Sun to other stars and to improve space-weather forecasting., Comment: Accepted at Machine Learning and the Physical Sciences workshop, NeurIPS 2022

Biomechanical and clinical gait research observes muscles and tendons in limbs to study their functions and behaviour. Therefore, movements of distinct anatomical landmarks, such as muscle-tendon junctions, are frequently measured. We propose a reliable and time efficient machine-learning approach to track these junctions in ultrasound videos and support clinical biomechanists in gait analysis. In order to facilitate this process, a method based on deep-learning was introduced. We gathered an extensive dataset, covering 3 functional movements, 2 muscles, collected on 123 healthy and 38 impaired subjects with 3 different ultrasound systems, and providing a total of 66864 annotated ultrasound images in our network training. Furthermore, we used data collected across independent laboratories and curated by researchers with varying levels of experience. For the evaluation of our method a diverse test-set was selected that is independently verified by four specialists. We show that our model achieves similar performance scores to the four human specialists in identifying the muscle-tendon junction position. Our method provides time-efficient tracking of muscle-tendon junctions, with prediction times of up to 0.078 seconds per frame (approx. 100 times faster than manual labeling). All our codes, trained models and test-set were made publicly available and our model is provided as a free-to-use online service on https://deepmtj.org/., Comment: in IEEE Transactions on Biomedical Engineering

Kanzelh\"ohe Observatory for Solar and Environmental Research (KSO) of the University of Graz (Austria) is in continuous operation since its foundation in 1943. Since the beginning its main task was the regular observation of the Sun in full disc. In this long time span covering almost seven solar cycles, a substantial amount of data was collected, which is made available online. In this paper we describe the separate processing steps from data acquisition to high level products for the different observing wavelengths. First of all we work out in detail the quality classification, which is important for further processing of the raw images. We show how we construct centre-to-limb variation (CLV) profiles and how we remove large scale intensity variations produced by the telescope optics in order to get images with uniform intensity and contrast. Another important point is an overview of the different data products from raw images to high contrast images with heliographic grids overlaid. As the data products are accessible via different sources we also present how to get information about the availability and how to obtain these data. Finally, in an appendix, we describe in detail the information in the FITS headers, the file naming and the data hierarchy., Comment: 39 pages, 21 figures, 3 tables

Coronal holes are the observational manifestation of the solar magnetic field open to the heliosphere and are of pivotal importance for our understanding of the origin and acceleration of the solar wind. Observations from space missions such as the Solar Dynamics Observatory now allow us to study coronal holes in unprecedented detail. Instrumental effects and other factors, however, pose a challenge to automatically detect coronal holes in solar imagery. The science community addresses these challenges with different detection schemes. Until now, little attention has been paid to assessing the disagreement between these schemes. In this COSPAR ISWAT initiative, we present a comparison of nine automated detection schemes widely-applied in solar and space science. We study, specifically, a prevailing coronal hole observed by the Atmospheric Imaging Assembly instrument on 2018 May 30. Our results indicate that the choice of detection scheme has a significant effect on the location of the coronal hole boundary. Physical properties in coronal holes such as the area, mean intensity, and mean magnetic field strength vary by a factor of up to 4.5 between the maximum and minimum values. We conclude that our findings are relevant for coronal hole research from the past decade, and are therefore of interest to the solar and space research community., Comment: Accepted for publication in The Astrophysical Journal. (Received January 20, 2021; Accepted March 25, 2021)

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. 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 H$\alpha$ 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. We apply our method to full-disk H$\alpha$ filtergrams from Kanzelh\"ohe Observatory recorded during 2012-2019 and demonstrate its capability to perform a reliable image quality assessment for various atmospheric conditions and instrumental effects, without the requirement of reference observations. 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.

Recording muscle tendon junction displacements during movement, allows separate investigation of the muscle and tendon behaviour, respectively. In order to provide a fully-automatic tracking method, we employ a novel deep learning approach to detect the position of the muscle tendon junction in ultrasound images. We utilize the attention mechanism to enable the network to focus on relevant regions and to obtain a better interpretation of the results. Our data set consists of a large cohort of 79 healthy subjects and 28 subjects with movement limitations performing passive full range of motion and maximum contraction movements. Our trained network shows robust detection of the muscle tendon junction on a diverse data set of varying quality with a mean absolute error of 2.55$\pm$1 mm. We show that our approach can be applied for various subjects and can be operated in real-time. The complete software package is available for open-source use via: https://github.com/luuleitner/deepMTJ, Comment: Accepted version to be published in 2020, 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Montreal, Canada