arXiv subjects

Robert Jarolim

Publications and source records attributed to Robert Jarolim.

At least 19 recordsLinked to original sources

arXiv2026

Comprehensive solar eruption analyses enabled by the tools of the SOLER project

Solar eruptions comprise of a multitude of phenomena such as flares, coronal mass ejections (CMEs), large-scale coronal waves, radio bursts, and energetic particles traveling through interplanetary space. These phenomena are observed with a variety of instrumentation, including remote sensing and in-situ detectors. Obtaining a global understanding of a solar eruption often requires the analysis of various of these different datasets, including a multitude of analysis and modeling tools and a wide range of expertise. Usually, such a comprehensive analysis can only be achieved by a skilled and broad team. The Energetic Solar Eruptions: Data and Analysis Tools (SOLER) project aims at creating a comprehensive analysis platform for the study of solar eruptions that allows a single user to easily apply analysis methods addressing various counterparts of the solar event. Therefore, each partner of the project developed Python-based software, including interfaces in the form of Jupyter Notebooks, which provides application examples and concise step-to-step documentation. In this paper we introduce the comprehensive solar-eruption-analysis infrastructure developed within the SOLER project. We explain where to find the software, how to use it, and give dedicated use-case examples of how to employ selected tools in a combined manner.

physics.space-ph↗

arXiv2026

Chromospheric magnetic field extrapolations reveal the flux-rope configuration of a solar filament

Solar eruptions are powered by the release of magnetic energy stored in the lower solar atmosphere, but the pre-eruptive magnetic configuration of filament channels remains difficult to determine. A central question is whether this energy is stored in a pre-existing magnetic flux rope or in a sheared arcade that forms a flux rope only during eruption. Resolving this ambiguity is critical for identifying instability thresholds and eruption triggers, yet photosphere-based extrapolations often provide insufficient constraints on the three-dimensional coronal field. Here, we introduce a data-driven magnetic field extrapolation framework that combines photospheric and chromospheric vector magnetograms in a unified multi-height optimization, while accounting for variable chromospheric formation heights and the 180{\deg} azimuthal ambiguity. Tests with radiative magnetohydrodynamic simulations show that photosphere-only extrapolations can misidentify the pre-eruptive magnetic configuration, whereas chromospheric vector constraints recover the three-dimensional structure substantially more accurately. Applied to multi-line spectropolarimetric observations of an active region filament obtained with the Swedish Solar Telescope, the method reveals a reconstructed magnetic field consistent with a pre-eruptive flux-rope configuration. These results show that chromospheric vector magnetic measurements can provide decisive constraints on filament magnetic configuration and open a path toward diagnosing magnetic-energy storage and instability in eruptive solar active regions.

astro-ph.SR↗

arXiv2026

SuNeRF-CME: Physics-Informed Neural Radiance Fields for Tomographic Reconstruction of Coronal Mass Ejections

Coronagraphic observations enable direct monitoring of coronal mass ejections (CMEs) through scattered light from free electrons, but determining the 3D plasma distribution from 2D imaging data is challenging due to the optically-thin plasma and the complex image formation processes. We introduce SuNeRF-CME, a framework for 3D tomographic reconstructions of the heliosphere using multi-viewpoint coronagraphic observations. The method leverages Neural Radiance Fields (NeRFs) to estimate the electron density in the heliosphere through a ray-tracing approach, while accounting for the underlying Thomson scattering of image formation. The model is optimized by iteratively fitting the time-dependent observational data. In addition, we apply physical constraints in terms of continuity, propagation direction, and speed of the heliospheric plasma to overcome limitations imposed by the sparse number of viewpoints. We utilize synthetic observations of a CME simulation to fully quantify the model's performance for different viewpoint configurations. The results demonstrate that our method can reliably estimate the CME parameters from only two viewpoints, with a mean velocity error of $3.01\pm1.94\%$ and propagation direction errors of $3.39\pm1.94^\circ$ in latitude and $1.76\pm0.79^\circ$ in longitude. We further show that our approach can achieve a full 3D reconstruction of the simulated CME from two viewpoints, where we correctly model the three-part structure, deformed CME front, and internal plasma variations. Additional viewpoints can be seamlessly integrated, directly enhancing the reconstruction of the plasma distribution in the heliosphere. This study underscores the value of physics-informed methods for reconstructing the heliospheric plasma distribution, paving the way for unraveling the dynamic 3D structure of CMEs and enabling advanced space weather monitoring.

astro-ph.SR↗

arXiv2026

Improving Solar Flare Soft X-ray Classification With FOXES: A Framework For Operational X-ray Emission Synthesis

The Geostationary Operational Environmental Satellite (GOES) solar soft X-ray (SXR) irradiance in the 1-8Å wavelength range is a long-standing measure of solar activity, used to define the classification of flare strengths. As a result, the flare class, along with the SXR light curves, are routinely used as a primary input for forecasting properties of space weather drivers, from coronal mass ejection speed to energetic particle output. However, the GOES SXR irradiance lacks spatial information, leading to known classification errors, such as misattributed flare locations during periods of high activity. Moreover, GOES only provides observations from Earth's orbit, hindering forecasting for other places in the heliosphere. Motivated by these limitations, we introduce the Framework for Operational X-ray Emission Synthesis (FOXES), a Vision Transformer-based approach for translating Extreme Ultraviolet (EUV) spatially-resolved observations into SXR irradiance predictions. The model produces two outputs: (1) a global 1-8Å SXR flux prediction and (2) per-patch flux contributions, which offer a spatially-resolved interpretation of where the model attributes SXR emission. Trained, validated, and tested on over 3200 hours of observations, FOXES has demonstrated a translational mean absolute error of 0.051 dex for integrated SXR measurements. FOXES has also shown promise in dissecting the solar background SXR flux during flaring and non-flaring events. Overall, this model paves the way for EUV-based spatially-resolved flare detection to be extended beyond Earth's line of sight. Such capabilities could lead to a more comprehensive flare catalog and enable a true multiviewpoint monitoring of solar activity.

astro-ph.SR↗

arXiv2026

Neural blind deconvolution to reconstruct high-resolution ground-based solar observations

Ground-based solar observations enable unprecedented spatial, spectral, and temporal resolution of the lower solar atmosphere, yet Earths turbulent atmosphere imposes significant limitations, requiring advanced post-facto image reconstruction. State-of-the-art reconstruction methods are based on restoring a burst of short exposure frames to a single observation. Limitations of these techniques arise due to the sparse information about the atmospheric point spread function (PSF) that degrade the observations and consequently the quality of reconstructions. We develop a novel image reconstruction method to achieve unprecedented spatial resolution from short exposure image bursts. This can provide high-quality reconstructions and therefore advance the study of the smallest spatial scales from the solar photosphere to the chromosphere. In this study, we present a novel approach for high-resolution solar image reconstruction based on physics-informed neural networks. In the training process, the neural network maps coordinate points directly to their corresponding intensity values while simultaneously updating the PSF parameters. The method convolves the true object from the neural network with the estimated PSFs and optimizes the network by minimizing the loss between the synthesized and real short-exposure image burst. This approach enables the simultaneous estimation of both the degrading PSF and the real high-resolution intensity distribution. We demonstrate the method on synthetic intensity data derived from a radiative MHD simulation and apply it to high-resolution observations from GREGOR and DKIST. Our results demonstrate the ability to reconstruct small-scale solar features that exceed the reconstruction performance of state-of-the-art reconstruction methods. With this approach we lay the foundation for future spatially varying PSFs.

astro-ph.SR↗

arXiv2025

New Bulgarian-Austrian project 'Joint observations and investigations of solar chromospheric and coronal activity'

We present the bilateral collaboration between Bulgarian and Austrian solar and space weather researchers on the topic of chromospheric and coronal activity. This new project will focus, on one hand, on the technical setup and calibration of the new Rozhen chromospheric telescope at the National Astronomical Observatory (NAO) by means of establishing optimal observational programs for different quiet-Sun and activity phenomena, automating the data collection and storage, implementing machine/deep learning models for feature recognition. The second aim is to carry out joint scientific analyses of solar phenomena using observations from ground-based instruments in both countries, and supplementary spacecraft data. The successful implementation of solar monitoring at NAO-Rozhen will facilitate the overall visibility of the Bulgarian instrument and generate interest towards astronomy and solar physics not only for PhD students and young scientists but also for the general public.

astro-ph.SR↗

arXiv2025

FOXES: A Framework For Operational X-ray Emission Synthesis

Understanding solar flares is critical for predicting space weather, as their activity shapes how the Sun influences Earth and its environment. The development of reliable forecasting methodologies of these events depends on robust flare catalogs, but current methods are limited to flare classification using integrated soft X-ray emission that are available only from Earth's perspective. This reduces accuracy in pinpointing the location and strength of farside flares and their connection to geoeffective events. In this work, we introduce a Vision Transformer (ViT)-based approach that translates Extreme Ultraviolet (EUV) observations into soft x-ray flux while also setting the groundwork for estimating flare locations in the future. The model achieves accurate flux predictions across flare classes using quantitative metrics. This paves the way for EUV-based flare detection to be extended beyond Earth's line of sight, which allows for a more comprehensive and complete solar flare catalog.

astro-ph.SR↗

arXiv2025

Image calibration between the Extreme Ultraviolet Imagers on Solar Orbiter and the Solar Dynamics Observatory

To study and monitor the Sun and its atmosphere, various space missions have been launched in the past decades. With the rapid improvement in technology and different mission requirements, the data products are subject to constant change. However, for long-term studies such as solar variability or multi-instrument investigations, uniform data series are required. In this study, we build on and expand the Instrument-to-Instrument translation (ITI) framework, which provides unpaired image translations. We apply the tool to data from the Extreme Ultraviolet Imager (EUI), specifically the Full Sun Imager (FSI) on Solar Orbiter (SolO) and the Atmospheric Imaging Assembly (AIA) on the Solar Dynamics Observatory (SDO). This approach allows us to create a homogeneous data set that combines the two extreme ultraviolet (EUV) imagers. We demonstrate that ITI is able to provide image calibration between SolO and SDO EUV imagers, independent of the varying orbital position of SolO. The comparison of the intercalibrated light curves derived from EUI and AIA shows that ITI can provide uniform data series that outperform a standard baseline calibration. We evaluate the perceptual similarity in terms of the Fréchet Inception Distance (FID), which demonstrates that ITI achieves a significant improvement of perceptual similarity between EUI and AIA. The study provides intercalibrated observations from SolO/EUI/FSI with SDO/AIA, enabling a homogeneous data set suitable for solar cycle studies and multi viewpoint investigations.

astro-ph.SR↗

arXiv2025

Magnetic structure and asymmetric eruption of a 500 Mm filament rooted in weak-field regions

We performed a detailed analysis of the magnetic structure and asymmetric eruption of a large (about 500 Mm) inverse S-shaped filament partially located in AR 13229 on February 24, 2023. We linked the filament's pre-eruptive magnetic configuration to its highly asymmetric eruption dynamics and the formation of a large-scale coronal dimming in a weak-field region (mean unsigned flux of about 5 G). To reconstruct the coronal magnetic field, we applied a physics-informed neural network (PINN)-based nonlinear force-free field (NLFFF) extrapolation method to a pre-eruption HMI vector magnetogram. The NLFFF extrapolation reveals a large-scale magnetic flux rope (MFR) of about 500 Mm in length, consistent with the filament. We identified an extended MFR footprint to the east that connects to the J-shaped flare ribbon, outlining where the coronal dimming began. Overlying strapping fields connect to the area into which the dimming and flare ribbon later expand. This configuration explains the formation of the dimming as a stationary flux rope and strapping flux dimming, with subsequent expansion driven by the growth of the MFR footprint through strapping-strapping reconnection. Conversely, the western filament leg shows multiple anchor points and strong overlying magnetic fields, which suppressed the dimming and partially confined the eruption on that side. The reconstructed pre-eruptive NLFFF configuration offers a clear physical explanation for the asymmetries seen in the eruption, flare geometry, and coronal dimming. This demonstrates that PINN-based NLFFF extrapolation can effectively model large-scale filaments extending into weak-field regions, enhancing our understanding of complex solar eruptions.

astro-ph.SR↗

arXiv2025

Deep learning image burst stacking to reconstruct high-resolution ground-based solar observations

Large aperture ground based solar telescopes allow the solar atmosphere to be resolved in unprecedented detail. However, observations are limited by Earths turbulent atmosphere, requiring post image corrections. Current reconstruction methods using short exposure bursts face challenges with strong turbulence and high computational costs. We introduce a deep learning approach that reconstructs 100 short exposure images into one high quality image in real time. Using unpaired image to image translation, our model is trained on degraded bursts with speckle reconstructions as references, improving robustness and generalization. Our method shows an improved robustness in terms of perceptual quality, especially when speckle reconstructions show artifacts. An evaluation with a varying number of images per burst demonstrates that our method makes efficient use of the combined image information and achieves the best reconstructions when provided with the full image burst.

astro-ph.SR↗

arXiv2025

PINN ME: A Physics-Informed Neural Network Framework for Accurate Milne-Eddington Inversions of Solar Magnetic Fields

Spectropolarimetric inversions of solar observations are fundamental for the estimation of the magnetic field in the solar atmosphere. However, instrumental noise, computational requirements, and varying levels of physical realism make it challenging to derive reliable solar magnetic field estimates. In this study, we present a novel approach for spectropolarimetric inversions based on Physics Informed Neural Networks (PINNs) to infer the photospheric magnetic field under the Milne-Eddington approximation (PINN ME). Our model acts as a representation of the parameter space, mapping input coordinates (t, x, y) to the respective spectropolarimetric parameters, which are used to synthesize the corresponding stokes profiles. By iteratively sampling coordinate points, synthesizing profiles, and minimizing the deviation from the observed stokes profiles, our method can find the set of Milne-Eddington parameters that best fit the observations. In addition, we directly include the point-spread-function to account for instrumental effects. We use a predefined parameter space as well as synthetic profiles from a radiative MHD simulation to evaluate the performance of our method and to estimate the impact of instrumental noise. Our results demonstrate that PINN ME achieves an intrinsic spatio-temporal coupling, which can largely mitigate observational noise and provides a memory-efficient inversion even for extended fields-of-view. Finally, we apply our method to observations and show that our method provides a high spatial coherence and can resolve small-scale features both in strong- and weak-field regions.

astro-ph.SR↗

arXiv2024

Magnetic Field Evolution of the Solar Active Region 13664

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.

astro-ph.SR↗

arXiv2024

Multipoint study of the rapid filament evolution during a confined C2 flare on 28 March 2022, leading to eruption

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° 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 Å 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.

astro-ph.SR↗

arXiv2024

A Universal Method for Solar Filament Detection from H-alpha Observations using Semi-supervised Deep Learning

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 Kanzelhöhe 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.

astro-ph.SR↗

arXiv2024

SuNeRF: 3D reconstruction of the solar EUV corona using Neural Radiance Fields

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.

astro-ph.SR↗

arXiv2024

Instrument-To-Instrument translation: Instrumental advances drive restoration of solar observation series via deep learning

The constant improvement of astronomical instrumentation provides the foundation for scientific discoveries. In general, these improvements have only implications forward in time, while previous observations do not benefit from this trend. Here we provide a general deep learning method that translates between image domains of different instruments (Instrument-To-Instrument translation; ITI). We demonstrate that the available data sets can directly profit from the most recent instrumental improvements, by applying our method to five different applications of ground- and space-based solar observations. We obtain 1) solar full-disk observations with unprecedented spatial resolution, 2) a homogeneous data series of 24 years of space-based observations of the solar EUV corona and magnetic field, 3) real-time mitigation of atmospheric degradations in ground-based observations, 4) a uniform series of ground-based H$α$ observations starting from 1973, 5) magnetic field estimates from the solar far-side based on EUV imagery. The direct comparison to simultaneous high-quality observations shows that our method produces images that are perceptually similar and match the reference image distribution.

astro-ph.SR↗

arXiv2024

The Link Between Non-Thermal Velocity and Free Magnetic Energy in Solar Flares

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 Alfvén wave propagation and isotropic turbulence have a greater influence.

astro-ph.SR↗

arXiv2023

Advancing solar magnetic field extrapolations through multi-height magnetic field measurements

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.

astro-ph.SR↗