相关论文: Improved AI-generated Solar Farside Magnetograms b…

Solar magnetic fields play a key role in understanding the nature of the coronal phenomena. Global coronal magnetic fields are usually extrapolated from photospheric fields, for which farside data is taken when it was at the frontside,…

In this dataset we provide a comprehensive collection of magnetograms (images quantifying the strength of the magnetic field) from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO). The dataset…

The analysis of waves in the visible side of the Sun allows the detection of active regions in the farside through local helioseismology techniques. The knowledge of the magnetism in the whole Sun, including the non-visible hemisphere, is…

Accurate modeling of the Sun's coronal magnetic field and solar wind structures require inputs of the solar global magnetic field, including both the near and far sides, but the Sun's far-side magnetic field cannot be directly observed.…

Seismic maps of the Sun's far hemisphere, computed from Doppler data from the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO) are now being used routinely to detect strong magnetic regions on the far…

Earth-side observations of solar p modes can be used to image and monitor magnetic activity on the Sun's far side. Here we use magnetograms of the far side obtained by the Polarimetric and Helioseismic Imager (PHI) onboard Solar Orbiter…

We provide a large image parameter dataset extracted from the Solar Dynamics Observatory (SDO) mission's AIA instrument, for the period of January 2011 through the current date, with the cadence of six minutes, for nine wavelength channels.…

Understanding and monitoring solar active regions is essential for operational space-weather forecasting and improved solar dynamo modeling. This requires comprehensive 360-degree observations of the Sun. While space-weather forecasting has…

Solar activity is usually caused by the evolution of solar magnetic fields. Magnetic field parameters derived from photospheric vector magnetograms of solar active regions have been used to analyze and forecast eruptive events such as solar…

Given the rarity of significant solar flares compared to smaller ones, training effective machine learning models for solar activity forecasting is challenging due to insufficient data. This study proposes using generative deep learning…

The continuous observation of the magnetic field by Solar Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI) produces numerous image sequences in time and space. These sequences provide data support for predicting the…

Image super-resolution has been an important subject in image processing and recognition. Here, we present an attention-aided convolutional neural network (CNN) for solar image super-resolution. Our method, named SolarCNN, aims to enhance…

The solar mean magnetic field (SMMF) provided by the Wilcox Solar Observatory (WSO) is compared with the SMMF acquired by the \textit{Helioseismic and Magnetic Imager} (HMI) onboard the \textit{Solar Dynamic Observatory} (SDO). We found…

Although the magnetic fields in the quiet Sun account for the majority of the magnetic energy in the solar photosphere, inferring their exact spatial distribution, origin, and evolution poses an important challenge because the signals lie…

Observations the Sun's photospheric magnetic field are often confined to the Sun-Earth line. Surface flux transport (SFT) models, such as the Advective Flux Transport (AFT) model, simulate the evolution of the photospheric magnetic field to…

Far-side images of solar active regions have become one of the routine products of helioseismic observations, and are of importance for space weather forecasting by allowing the detection of sunspot regions before they become visible on the…

To model the structure and dynamics of the heliosphere well enough for high-quality forecasting, it is essential to accurately estimate the global solar magnetic field used as inner boundary condition in solar wind models. However, our…

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 radioheliograph image is essential for the study of solar short term activities and long term variations, while the continuity and granularity of radioheliograph data is not so ideal, due to the short visible time of the sun and the…

Maps of the magnetic field at the Sun's surface are commonly used as boundary conditions in space-weather modeling. However, continuous observations are only available from the Sun's Earth-facing side. One commonly used approach to mitigate…