Related papers: Solar activity classification based on Mg II spect…

The Geostationary Operational Environmental Satellite (GOES) solar soft X-ray (SXR) irradiance in the 1-8{\AA} wavelength range is a long-standing measure of solar activity, used to define the classification of flare strengths. As a result,…

The most intense solar energetic particle events are produced by coronal mass ejections (CMEs) accompanied by intense type II radio bursts below 15 MHz. Understanding where these type II bursts are generated relative to an erupting CME…

Previous studies have found that red giants (RGs) in close binary systems undergoing spin-orbit resonance exhibit an enhanced level of magnetic activity with respect to single RGs rotating at the same rate, from measurements of photometric…

Solar flares, especially C, M, and X class, pose significant risks to satellite operations, communication systems, and power grids. We present a novel approach for predicting extreme solar flares using HMI intensitygrams and magnetograms.…

Millimter (mm) frequencies are primarily sensitive to thermal emission from layers across the stellar chromosphere up to the transition region, while metrewave (radio) frequencies probe the coronal heights. Together the mm and radio band…

Most real-world classification problems deal with imbalanced datasets, posing a challenge for Artificial Intelligence (AI), i.e., machine learning algorithms, because the minority class, which is of extreme interest, often proves difficult…

(Abridged) Aims: In this paper, we seek to establish the suitability of imaging spectroscopy performed in the Ca II 854.2 nm line as a means to investigate the solar chromosphere at high resolution. Methods: We utilize monochromatic images…

Solar flares, especially the M- and X-class flares, are often associated with coronal mass ejections (CMEs). They are the most important sources of space weather effects, that can severely impact the near-Earth environment. Thus it is…

We attempt to forecast M-and X-class solar flares using a machine-learning algorithm, called Support Vector Machine (SVM), and four years of data from the Solar Dynamics Observatory's Helioseismic and Magnetic Imager, the first instrument…

We developed a flare prediction model using machine learning, which is optimized to predict the maximum class of flares occurring in the following 24 h. Machine learning is used to devise algorithms that can learn from and make decisions on…

We employ the XGBoost machine learning (ML) method for the morphological classification of galaxies into two (early-type, late-type) and five (E, S0--S0a, Sa--Sb, Sbc--Scd, Sd--Irr) classes, using a combination of non-parametric…

Ahead-of-time forecasting of the output power of power plants is essential for the stability of the electricity grid and ensuring uninterrupted service. However, forecasting renewable energy sources is difficult due to the chaotic behavior…

The Gaia Radial Velocity Spectrometer provides the unique opportunity of a spectroscopic analysis of millions of stars at medium-resolution in the near-infrared. This wavelength range includes the Ca II infrared triplet (IRT), which is a…

This paper addresses the pressing need for an accurate solar energy prediction model, which is crucial for efficient grid integration. We explore the influence of the Air Quality Index and weather features on solar energy generation,…

The purpose of this letter is to address a blind-spot in our knowledge of solar active region statistics. To the best of our knowledge there are no published results showing the variation of the Mount Wilson magnetic classifications as a…

Cloud detection is essential for atmospheric retrievals, climate studies, and weather forecasting. We analyze infrared radiances from the Infrared Atmospheric Sounding Interferometer (IASI) onboard Meteorological Operational (MetOp)…

Measuring the performance of solar energy and heat transfer systems requires a lot of time, economic cost and manpower. Meanwhile, directly predicting their performance is challenging due to the complicated internal structures. Fortunately,…

We introduce the concept of a Background-subtracted Solar Activity Map (BaSAM) as a new quantitative tool to assess and visualize the temporal variation of the photospheric magnetic field and the UV 160 nm intensity. The method utilizes…

The immense amount of time series data produced by astronomical surveys has called for the use of machine learning algorithms to discover and classify several million celestial sources. In the case of variable stars, supervised learning…

The observed magnetic field on the solar surface is characterized by a very complex spatial and temporal behavior. Although feature-tracking algorithms have allowed us to deepen our understanding of this behavior, subjectivity plays an…