arXiv subjects

Christoph Schirninger

Publications and source records attributed to Christoph Schirninger.

7 recordsLinked to original sources

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

Flares on TRAPPIST-1 reveal the spectrum of magnetic features on its surface

TRAPPIST-1 is an M8 dwarf hosting seven known exoplanets and is currently one of the most frequently observed targets of the James Webb Space Telescope (JWST). However, it is notoriously active, and its surface is believed to be covered by magnetic features that contaminate the planetary transmission spectra. The radiative spectra of these magnetic features are needed to clean transmission spectra, but they currently remain unknown. Here, we develop a new approach for measuring these spectra using time-resolved JWST/NIRISS observations. We detect a persistent post-flare enhancement in the spectral flux of TRAPPIST-1. Our analysis rules out lingering flare decay as the cause of the flux enhancement and, thus, points to structural changes on the stellar surface induced by flares. We suggest that the flaring event triggers the disappearance of (part of) a dark magnetic feature, producing a net brightening. This suggestion is motivated by solar data: flare-induced disappearance of magnetic features on the solar surface has been directly detected in high spatial resolution images, and our analysis shows that this process produces changes in solar brightness very similar to those we observe on TRAPPIST-1. The proposed explanation for the flux enhancement enables, to our knowledge, the first measurement of the spectrum of a magnetic feature on an M8 dwarf. Our analysis indicates that the disappearing magnetic feature is cooler than the TRAPPIST-1 photosphere, but by at most a few hundred kelvins.

astro-ph.EP↗

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↗