Related papers: Improved detection of farside solar active regions…
This paper presents a post hoc analysis of a deep learning-based full-disk solar flare prediction model. We used hourly full-disk line-of-sight magnetogram images and selected binary prediction mode to predict the occurrence of…
Understanding the governing mechanism of solar magnetism remains an outstanding challenge in astrophysics. Seismology is the most compelling technique with which to infer the internal properties of the Sun and stars. Waves in the Sun,…
Magnetic flux generated within the solar interior emerges to the surface, forming active regions (ARs) and sunspots. Flux emergence may trigger explosive events, such as flares and coronal mass ejections and therefore understanding…
Previous helioseismology of sunspots has been sensitive to both the structural and magnetic aspects of sunspot structure. We aim to develop a technique that is insensitive to the magnetic component so the two aspects can be more readily…
One of the main goals of solar physics is the timely identification of eruptive active regions. Space missions such as Solar Orbiter or future Space Weather forecasting missions would largely benefit from this achievement. Our aim is to…
With the rapid development of telescopes, both temporal cadence and the spatial resolution of observations are increasing. This in turn generates vast amount of data, which can be efficiently searched only with automated detections in order…
Inferring interior properties of the Sun from photospheric measurements of the seismic wavefield constitutes the helioseismic inverse problem. Deviations in seismic measurements (such as wave travel times) from their fiducial values…
The continuous high spatial-resolution Doppler observation of the Sun by Solar Dynamics Observatory / Helioseismic and Magnetic Imager allows us to compute helioseismic k-omega power-spectrum diagram using only oscillations inside a…
We analyze a solar active region observed by the Hinode CaII H line using the time-distance helioseismology technique, and infer wave-speed perturbation structures and flow fields beneath the active region with a high spatial resolution.…
Accurate, reliable solar flare prediction is crucial for mitigating potential disruptions to critical infrastructure, while predicting solar flares remains a significant challenge. Existing methods based on heuristic physical features often…
One of the risks of large-scale geologic carbon sequestration is the potential migration of fluids out of the storage formations. Accurate and fast detection of this fluids migration is not only important but also challenging, due to the…
Seasonal forecasting remains challenging due to the inherent chaotic nature of atmospheric dynamics. This paper introduces DeepSeasons, a novel deep learning approach designed to enhance the accuracy and reliability of seasonal forecasts.…
This study aims to evaluate the performance of deep learning models in predicting $\geq$M-class solar flares with a prediction window of 24 hours, using hourly sampled full-disk line-of-sight (LoS) magnetogram images, particularly focusing…
Geoscience and seismology have utilized the most advanced technologies and equipment to monitor seismic events globally from the past few decades. With the enormous amount of data, modern GPU-powered deep learning presents a promising…
Operational flare forecasting aims at providing predictions that can be used to make decisions, typically at a daily scale, about the space weather impacts of flare occurrence. This study shows that video-based deep learning can be used for…
Ahead-of-time forecasting of incident solar-irradiance on a panel is indicative of expected energy yield and is essential for efficient grid distribution and planning. Traditionally, these forecasts are based on meteorological physics…
Current helicity and twist of solar magnetic fields are important quantities to characterize the dynamo mechanism working in the convection zone of the Sun. We have carried out a statistical study on the current helicity of solar active…
We present an automated system for detecting, tracking, and cataloging emerging active regions throughout their evolution and decay using SOHO Michelson Doppler Interferometer (MDI) magnetograms. The SolarMonitor Active Region Tracking…
Machine learning is starting to be used in almost every industry and academic research, and solar physics is no exception. A newly developed machine learning model named MagNet helps us to tackle some of the most serious challenges in data…
The SoHO spacecraft made astrophysicists achieve a major breakthrough in the knowledge of the Sun. In helioseismology, both GOLF and MDI experiments aboard SoHO greatly improve the accuracy of seismic data. More specifically, the detection…