中文
相关论文

相关论文: Predicting Solar Energetic Particles Using SDO/HMI…

200 篇论文

Solar flares and coronal mass ejections (CMEs) cause immediate and adverse effects on the interplanetary space and geospace. The deeper understanding of the mechanisms that produce them and the construction of efficient prediction schemes…

太阳与恒星天体物理 · 物理学 2022-10-12 Ioannis Kontogiannis

Prediction of solar flares is an important task in solar physics. The occurrence of solar flares is highly dependent on the structure and the topology of solar magnetic fields. A new method for predicting large (M and X class) flares is…

太阳与恒星天体物理 · 物理学 2016-12-28 Abbas Raboonik , Hossein Safari , Nasibe Alipour , Michael S. Wheatland

Although space weather events may not directly affect human life, they have the potential to inflict significant harm upon our communities. Harmful space weather events can trigger atmospheric changes that result in physical and economic…

太阳与恒星天体物理 · 物理学 2024-05-07 Shlesh Sakpal

Solar flares are a primary driver of space weather, and forecasting their occurrence remains a significant challenge. This paper presents a novel flare prediction model based on topologically derived photospheric magnetic parameters. We…

太阳与恒星天体物理 · 物理学 2025-12-18 Thomas Williams , Christopher B. Prior , David MacTaggart , D. Shaun Bloomfield

We study the influence of the large-scale interplanetary magnetic field configuration on the solar energetic particles (SEPs) as detected at different satellites near Earth and on the correlation of their peak intensities with the parent…

太阳与恒星天体物理 · 物理学 2014-03-05 R. Miteva , K. -L. Klein , O. Malandraki , G. Dorrian

Solar Energetic Particles (SEPs) are charged particles accelerated within the solar atmosphere or the interplanetary space by explosive phenomena such as solar flares or Coronal Mass Ejections (CMEs). Once injected into the interplanetary…

The development of accurate forecasts of solar eruptive activity has become increasingly important for preventing potential impacts on space technologies and exploration. Therefore, it is crucial to detect Active Regions (ARs) before they…

This study aims to evaluate the performance of deep learning models in predicting $\geq$M-class solar flares with a prediction window of 24 hours, using hourly sampled full-disk line-of-sight (LoS) magnetogram images, particularly focusing…

太阳与恒星天体物理 · 物理学 2024-06-18 Chetraj Pandey , Rafal A. Angryk , Berkay Aydin

The need of real-time of monitoring and alerting systems for Space Weather hazards has grown significantly in the last two decades. One of the most important challenge for space mission operations and planning is the prediction of solar…

太阳与恒星天体物理 · 物理学 2024-06-19 Mirko Stumpo , Monica Laurenza , Simone Benella , Maria Federica Marcucci

Solar flares are among the most severe space weather phenomena, and they have the capacity to generate radiation storms and radio disruptions on Earth. The accurate prediction of solar flare events remains a significant challenge, requiring…

太阳与恒星天体物理 · 物理学 2023-10-31 Vysakh P. A. , Prateek Mayank

We developed a solar flare prediction model using a deep neural network (DNN), named Deep Flare Net (DeFN). The model can calculate the probability of flares occurring in the following 24 h in each active region, which is used to determine…

太阳与恒星天体物理 · 物理学 2018-05-23 Naoto Nishizuka , Komei Sugiura , Yuki Kubo , Mitsue Den , Mamoru Ishii

Observational pre-cursors of large solar flares provide a basis for future operational systems for forecasting. Here, we study the evolution of the normalized emergence (EM), shearing (SH) and total (T) magnetic helicity flux components for…

太阳与恒星天体物理 · 物理学 2022-02-09 Sz. Soós , M. B. Korsós , H. Morgan , R. Erdélyi

We developed a reliable probabilistic solar flare forecasting model using a deep neural network, named Deep Flare Net-Reliable (DeFN-R). The model can predict the maximum classes of flares that occur in the following 24 h after observing…

太阳与恒星天体物理 · 物理学 2020-09-02 Naoto Nishizuka , Yûki Kubo , Komei Sugiura , Mitsue Den , Mamoru Ishii

We introduce a novel deep learning framework based on Long Short-Term Memory (LSTM) networks to predict galactic cosmic-ray spectra on a one-day-ahead basis by leveraging historical solar activity data, overcoming limitations inherent in…

高能天体物理现象 · 物理学 2025-01-13 Yi-Lun Du , Xiaojian Song , Xi Luo

The precise physical process that triggers solar flares is not currently understood. Here we attempt to capture the signature of this mechanism in solar image data of various wavelengths and use these signatures to predict flaring activity.…

太阳与恒星天体物理 · 物理学 2018-03-14 Eric Jonas , Monica G. Bobra , Vaishaal Shankar , J. Todd Hoeksema , Benjamin Recht

Accurate and reliable predictions of solar flares are essential due to their potentially significant impact on Earth and space-based infrastructure. Although deep learning models have shown notable predictive capabilities in this domain,…

机器学习 · 计算机科学 2024-11-28 Temitope Adeyeha , Chetraj Pandey , Berkay Aydin

Solar flare prediction is a central problem in space weather forecasting and recent developments in machine learning and deep learning accelerated the adoption of complex models for data-driven solar flare forecasting. In this work, we…

机器学习 · 计算机科学 2023-09-12 Chetraj Pandey , Anli Ji , Rafal A. Angryk , Berkay Aydin

Solar flares are a fundamental component of solar eruptive events (SEEs; along with solar energetic particles, SEPs, and coronal mass ejections, CMEs). Flares are the first component of the SEE to impact our atmosphere, which can set the…

We study the solar energetic particle (SEP) event observed on 9 October 2021, by multiple spacecraft including Solar Orbiter (SolO). The event was associated with an M1.6 flare, a coronal mass ejection (CME) and a shock wave. During the…

Coronal mass ejections (CMEs) are major drivers of stellar space weather and can strongly influence the habitability of exoplanets. However, compared to the frequent occurrence of white-light flares, confirmed stellar CMEs remain extremely…

太阳与恒星天体物理 · 物理学 2025-12-18 Yu Shi , Hong-Peng Lu , Li-Yun Zhang , Tian-Hao Su , Chao Tan