Related papers: Classifying different types of solar wind plasma w…

Understanding the generation and development of the continuous outflow from the Sun requires tracing the physical conditions from deep in the corona to the heliosphere. Detailed global observations of plasma state variables and the magnetic…

Macroscopic wave and oscillatory phenomena ubiquitously detected in the plasma of the corona of the Sun are interpreted in terms of magnetohydrodynamic theory. Fast and slow magnetoacoustic waves are clearly distinguished in observations.…

The high penetration of volatile renewable energy sources such as solar make methods for coping with the uncertainty associated with them of paramount importance. Probabilistic forecasts are an example of these methods, as they assist…

A key challenge in the development of materials for the next generation of solar cells, sensors and transistors is linking macroscopic device performance to underlying microscopic properties. For years, fabrication of devices has been…

This paper briefly reviews a number of fundamental measurements that need to be made in order to characterize turbulence in space plasmas such as the solar wind. It has long been known that many of these quantities require simultaneous…

By using our previous results of magnetohydrodynamical simulations for the solar wind from open flux tubes, I discuss how the solar wind in the past is different from the current solar wind. The simulations are performed in fixed…

The solar wind, a stream of charged particles originating from the Sun and transcending interplanetary space, poses risks to technology and astronauts. In this work, we develop a prediction model to forecast the solar wind speed at the…

The paper presents a new method for deriving turbulent heating of the solar wind using plasma moments and magnetic field data. We develop the method and then apply it to compute the turbulent heating of the solar wind proton plasma at 1AU.…

In the corona, plasma is accelerated to hundreds of kilometers per second, and heated to temperatures hundreds of times hotter than the Sun's surface, before it escapes to form the solar wind. Decades of space-based experiments have shown…

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…

The solar wind (SW) is a vital component of space weather, providing a background for solar transients such as coronal mass ejections, stream interaction regions, and energetic particles propagating toward Earth. Accurate prediction of…

This article implements a Convolutional Neural Network (CNN)-based deep learning model for solar-wind prediction. Images from the Atmospheric Imaging Assembly (AIA) at 193\.A wavelength are used for training. Solar-wind speed is taken from…

We present a scalable machine learning framework for analyzing Parker Solar Probe (PSP) solar wind data using distributed processing and the quantum-inspired Kernel Density Matrices (KDM) method. The PSP dataset (2018--2024) exceeds 150 GB,…

Convolutional Neural Networks (CNN) possess many positive qualities when it comes to spatial raster data. Translation invariance enables CNNs to detect features regardless of their position in the scene. However, in some domains, like…

Randomization-based Machine Learning methods for prediction are currently a hot topic in Artificial Intelligence, due to their excellent performance in many prediction problems, with a bounded computation time. The application of…

Polytropic models of stellar winds remain to be useful tools because they allow for a simple description of the energy balance of the expanding plasma without explicitly specifying potentially complex energy transport processes like, e.g.,…

Machine learning techniques have been widely used in attempts to forecast several solar datasets. Most of these approaches employ supervised machine learning algorithms which are, in general, very data hungry. This hampers the attempts to…

Current operational forecasts of solar eruptions are made by human experts using a combination of qualitative shape-based classification systems and historical data about flaring frequencies. In the past decade, there has been a great deal…

Solar wind back-mapping is a combination of ballistic mapping and magnetic mapping. By examining the different model ingredients that can affect the derived back-mapped position, we aim to provide a more precise estimate of the source…

In this paper, we consider incorporating data associated with the sun's north and south polar field strengths to improve solar flare prediction performance using machine learning models. When used to supplement local data from active…