arXiv Computer Vision
MAGiDiff: Sampling the Photospheric Vector Field from UV/EUV Filtergrams
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arXiv Machine Learning
Jul 23
A Deep Learning Framework for Predicting Solar EUV Irradiance During Significant Flares
arXiv:2607. 19597v1 Announce Type: cross Abstract: We present FlareEUV, a multimodal deep learning framework for predicting daily extreme ultraviolet (EUV) irradiance at 6.
arXiv Machine Learning
Sep 22
Aurora Hunter: A Two-Stage Framework for Probabilistic Visibility Forecasting
arXiv:2605.24038v3 Announce Type: replace-cross Abstract: Aurora visibility at a given location requires two physically distinct conditions to hold at once: aurora occurring overhead, governed by sol...
arXiv AI
Sep 17
Physics-Informed Neural Networks for Fast Multilayer Spectral Inversion of H{\alpha} 6562.8 A and Ca II 8542.1 A Spectra
The paper presents a physics-informed neural network (PINN) that accelerates multilayer spectral inversion (MLSI) of solar chromospheric lines Hα 6562.8 Å and Ca II 8542.1 Å. The PINN predicts MLSI parameters from observed line profiles and uses a differentiable forward model to synthesize spectra, trained in two stages—first with spectral reconstruction loss, then fine‑tuned with conventional MLSI results on a single reference image. Applied to Fast Imaging Solar Spectrograph data, the method reproduces key spatial structures and achieves a 12–60× speedup, processing a raster in 5–15 s versus 3–5 min for traditional MLSI.
arXiv AI
Jun 9
Reconstructing Synthetic SDO/AIA 193 A EUV Images from He I 10830 A Observations with Diffusion Model Translator
arXiv:2606. 08652v1 Announce Type: cross Abstract: Routine full-disk EUV imaging has been available only since the modern era, such as SOHO and SDO.
arXiv AI
Sep 24
PISCES: Physics-Informed Solar-wind Convolutional autoEncoder for Space-weather Anomaly Detection and Early Warning
PISCES is a physics‑informed convolutional autoencoder designed to detect solar‑wind transients for space‑weather early warning. Trained on OMNI solar‑wind data without catalog labels, its loss incorporates magnetic field consistency, temperature‑velocity relations, the Parker spiral angle, and temporal smoothness penalties. During inference, PISCES decomposes the anomaly score into magnetic, plasma, physics‑relation, and residual components, enabling alarms that can precede observed sudden commencements and positive sudden impulses.
arXiv Computer Vision
Sep 24
Super-Resolution of Solar Magnetograms via Adaptive Stratified Ensemble Learning with Uncertainty Estimation
The paper presents a method for single‑image super‑resolution of solar magnetograms, converting low‑resolution SOHO/MDI data into high‑resolution SDO/HMI line‑of‑sight images. It uses a modified RRDBNet architecture initialized with ESRGAN weights and introduces an adaptive stratified specialist ensemble (SSE) that trains three specialist networks on different image complexity strata, guided by a lightweight router and uncertainty estimation. Experiments show the ensemble outperforms related approaches, improving reconstruction quality across heterogeneous space‑based instruments.