The origin, early evolution and predictability of solar eruptions
Coronal mass ejections (CMEs) were discovered in the early 1970s when space-borne
coronagraphs revealed that eruptions of plasma are ejected from the Sun. Today, it is known …
coronagraphs revealed that eruptions of plasma are ejected from the Sun. Today, it is known …
Coronal mass ejections over solar cycles 23 and 24
PL Lamy, O Floyd, B Boclet, J Wojak, H Gilardy… - Space Science …, 2019 - Springer
We present a statistical analysis of solar coronal mass ejections (CMEs) based on 23 years
of quasi-continuous observations with the LASCO coronagraph, thus covering two complete …
of quasi-continuous observations with the LASCO coronagraph, thus covering two complete …
Deep flare net (DeFN) model for solar flare prediction
N Nishizuka, K Sugiura, Y Kubo, M Den… - The Astrophysical …, 2018 - iopscience.iop.org
We developed a solar flare prediction model using a deep neural network (DNN) named
Deep Flare Net (DeFN). This model can calculate the probability of flares occurring in the …
Deep Flare Net (DeFN). This model can calculate the probability of flares occurring in the …
Predicting solar flares using a long short-term memory network
We present a long short-term memory (LSTM) network for predicting whether an active
region (AR) would produce a ϒ-class flare within the next 24 hr. We consider three ϒ …
region (AR) would produce a ϒ-class flare within the next 24 hr. We consider three ϒ …
Solar flare prediction model with three machine-learning algorithms using ultraviolet brightening and vector magnetograms
N Nishizuka, K Sugiura, Y Kubo, M Den… - The Astrophysical …, 2017 - iopscience.iop.org
We developed a flare prediction model using machine learning, which is optimized to predict
the maximum class of flares occurring in the following 24 hr. Machine learning is used to …
the maximum class of flares occurring in the following 24 hr. Machine learning is used to …
A comparison of flare forecasting methods. I. Results from the “all-clear” workshop
G Barnes, KD Leka, CJ Schrijver, T Colak… - The Astrophysical …, 2016 - iopscience.iop.org
Solar flares produce radiation that can have an almost immediate effect on the near-Earth
environment, making it crucial to forecast flares in order to mitigate their negative effects. The …
environment, making it crucial to forecast flares in order to mitigate their negative effects. The …
Solar flare prediction using advanced feature extraction, machine learning, and feature selection
OW Ahmed, R Qahwaji, T Colak, PA Higgins… - Solar Physics, 2013 - Springer
Novel machine-learning and feature-selection algorithms have been developed to study: i)
the flare-prediction-capability of magnetic feature (MF) properties generated by the recently …
the flare-prediction-capability of magnetic feature (MF) properties generated by the recently …
The SPoCA-suite: Software for extraction, characterization, and tracking of active regions and coronal holes on EUV images
Context. Precise localization and characterization of active regions (AR) and coronal holes
(CH) as observed by extreme ultra violet (EUV) imagers are crucial for a wide range of solar …
(CH) as observed by extreme ultra violet (EUV) imagers are crucial for a wide range of solar …
AutoTAB: Automatic Tracking Algorithm for Bipolar Magnetic Regions
Bipolar magnetic regions (BMRs) provide crucial information about solar magnetism. They
exhibit varying morphology and magnetic properties throughout their lifetime, and studying …
exhibit varying morphology and magnetic properties throughout their lifetime, and studying …
[HTML][HTML] Automatic sunspot detection through semantic and instance segmentation approaches
A Mourato, J Faria, R Ventura - Engineering Applications of Artificial …, 2024 - Elsevier
The solar influence on space weather and terrestrial environment is substantial. Strong
geomagnetic storm activity can significantly affect astronauts in orbit, communications and …
geomagnetic storm activity can significantly affect astronauts in orbit, communications and …