相关论文: Solar activity classification based on Mg II spect…
We develop a mixed Long Short Term Memory (LSTM) regression model to predict the maximum solar flare intensity within a 24-hour time window 0$\sim$24, 6$\sim$30, 12$\sim$36 and 24$\sim$48 hours ahead of time using 6, 12, 24 and 48 hours of…
The dependence of stellar magnetic activity on stellar parameters would be inspired by the chromospheric activity studies based on the large-scale spectroscopic surveys. The Ca II H and K lines are employed to construct indicators for…
The goal is to compare the intricate details of the magnetic and flow fields around two solar pores, where one is part of an active region and the other is an isolated pore, with a secondary goal of demonstrating the scientific capabilities…
Inversion codes allow reconstructing a model atmosphere from observations. With the inclusion of optically thick lines that form in the solar chromosphere, such modelling is computationally very expensive because a non-LTE evaluation of the…
The emergence of a new discipline called space weather, which aims at understanding and predicting the impact of solar activity on the terrestrial environment and on technological systems, has led to a growing need for analysing solar…
We analysed state-of-the-art observations of the solar atmosphere to investigate the dependence of the \ca brightness of several solar features on spectral bandwidth and spatial resolution of the data. Specifically, we study data obtained…
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…
Solar energy is one of the most economical and clean sustainable energy sources on the planet. However, the solar energy throughput is highly unpredictable due to its dependency on a plethora of conditions including weather, seasons, and…
Our knowledge of the low solar atmosphere, i.e., the photosphere and chromosphere, is based on the knowledge gained from the observations and the theoretical and numerical modeling of these layers. In this sense, the thermodynamical and…
We proposed a machine learning approach to identify and distinguish dusty stellar sources employing supervised and unsupervised methods and categorizing point sources, mainly evolved stars, using photometric and spectroscopic data collected…
The projection of shadows from moving clouds in the troposphere impacts energy generation in power grids using photovoltaic systems. This investigation proposes an efficient method of data processing for the statistical quantification of…
The Legacy Survey of Space and Time, to be conducted with the Vera C. Rubin Observatory, is poised to revolutionize our understanding of the Solar System by providing an unprecedented wealth of data on various objects, including the elusive…
The increasing global demand for clean and environmentally friendly energy resources has caused increased interest in harnessing solar power through photovoltaic (PV) systems for smart grids and homes. However, the inherent unpredictability…
The Mg II k emission line is a good indicator of the level of chromospheric activity in late-type stars. We investigate the dependence of this activity indicator on fundamental stellar parameters. To this purpose we use IUE observations of…
The use of supervised methods in space science have demonstrated powerful capability in classification tasks, but unsupervised methods have been less utilized for the clustering of spacecraft observations. We use a combination of…
Solar radio bursts are some of the brightest emissions at radio frequencies in the solar system. The emission mechanisms that generate these bursts offer a remote insight into physical processes in solar coronal plasma, while fine spectral…
Spectral lines allow us to probe the thermodynamics of the solar atmosphere, but the shape of a single spectral line may be similar for different thermodynamic solutions. Multiline analyses are therefore crucial, but computationally…
Solar forecasting accuracy is affected by weather conditions, and weather awareness forecasting models are expected to improve the performance. However, it may not be available and reliable to classify different forecasting tasks by using…
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 CDMS-II experiment operates 19 germanium detectors with a mass of 250g each in a very low background environment. Originally designed for the search for Dark Matter the experiment can also detect solar axions by Primakoff conversion to…