Related papers: Identifying and Tracking Solar Magnetic Flux Eleme…
With the advent of deep learning for computer vision tasks, the need for accurately labeled data in large volumes is vital for any application. The increasingly available large amounts of solar image data generated by the Solar Dynamic…
The solar wind consists of charged particles ejected from the Sun into interplanetary space and towards Earth. Understanding the magnetic field of the solar wind is crucial for predicting future space weather and planetary atmospheric loss.…
The paper presents a reliable method using deep learning to recognize solar filaments in H-alpha full-disk solar images automatically. This method cannot only identify filaments accurately but also minimize the effects of noise points of…
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…
The F10.7 and F30 solar indices are the solar radio fluxes measured at wavelengths of 10.7 cm and 30 cm, respectively, which are key indicators of solar activity. F10.7 is valuable for explaining the impact of solar ultraviolet (UV)…
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…
Investigating the properties of magnetic flux emergence is one of the most important problems of solar physics. In this study we present a newly developed deep-focus time-distance measurement scheme which is able to detect strong emerging…
Solar flare prediction plays an important role in understanding and forecasting space weather. The main goal of the Helioseismic and Magnetic Imager (HMI), one of the instruments on NASA's Solar Dynamics Observatory, is to study the origin…
Studies on the dynamics of solar filaments have significant implications for understanding their formation, evolution, and eruption, which are of great importance for space weather warning and forecasting. The H$\alpha$ Imaging Spectrograph…
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…
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 volume of data being collected in solar physics has exponentially increased over the past decade and with the introduction of the $\textit{Daniel K. Inouye Solar Telescope}$ (DKIST) we will be entering the age of petabyte solar data.…
Clouds and aerosols provide unique insight into the chemical and physical processes of gas-giant planets. Mapping and characterizing the spectral features indicative of the cloud structure and composition enables an understand-ing of a…
With the dramatic rise in high-quality galaxy data expected from Euclid and Vera C. Rubin Observatory, there will be increasing demand for fast high-precision methods for measuring galaxy fluxes. These will be essential for inferring the…
In this work, we present SoFT: Solar Feature Tracking, a novel feature-tracking tool developed in Python and designed to detect, identify, and track magnetic elements in the solar atmosphere. It relies on a watershed segmentation algorithm…
Deep learning models have gained increasing prominence in recent years in the field of solar pho-tovoltaic (PV) forecasting. One drawback of these models is that they require a lot of high-quality data to perform well. This is often…
This report presents the results of the DeepSolaris project that was carried out under the ESS action 'Merging Geostatistics and Geospatial Information in Member States'. During the project several deep learning algorithms were evaluated to…
Organic semiconductors are promising materials for cheap, scalable and sustainable electronics, light-emitting diodes and photovoltaics. For organic photovoltaic cells, it is a challenge to find compounds with suitable properties in the…
Investigating the solar magnetic field is crucial to understand the physical processes in the solar interior as well as their effects on the interplanetary environment. We introduce a novel method to predict the evolution of the solar…
We present a set of tools for detecting small-scale solar magnetic cancellations and the disk counterpart of type II spicules (the so-called Rapid Blueshifted Excursions (RBEs)), using line-of-sight photospheric magnetograms and…