234 research outputs found
Identifying Solar Flare Precursors Using Time Series of SDO/HMI Images and SHARP Parameters
We present several methods towards construction of precursors, which show
great promise towards early predictions, of solar flare events in this paper. A
data pre-processing pipeline is built to extract useful data from multiple
sources, Geostationary Operational Environmental Satellites (GOES) and Solar
Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI), to prepare
inputs for machine learning algorithms. Two classification models are
presented: classification of flares from quiet times for active regions and
classification of strong versus weak flare events. We adopt deep learning
algorithms to capture both the spatial and temporal information from HMI
magnetogram data. Effective feature extraction and feature selection with raw
magnetogram data using deep learning and statistical algorithms enable us to
train classification models to achieve almost as good performance as using
active region parameters provided in HMI/Space-Weather HMI-Active Region Patch
(SHARP) data files. Case studies show a significant increase in the prediction
score around 20 hours before strong solar flare events
Towards Interpretable Solar Flare Prediction with Attention-based Deep Neural Networks
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 developed an attention-based deep learning model as an improvement
over the standard convolutional neural network (CNN) pipeline to perform
full-disk binary flare predictions for the occurrence of M1.0-class
flares within the next 24 hours. For this task, we collected compressed images
created from full-disk line-of-sight (LoS) magnetograms. We used data-augmented
oversampling to address the class imbalance issue and used true skill statistic
(TSS) and Heidke skill score (HSS) as the evaluation metrics. Furthermore, we
interpreted our model by overlaying attention maps on input magnetograms and
visualized the important regions focused on by the model that led to the
eventual decision. The significant findings of this study are: (i) We
successfully implemented an attention-based full-disk flare predictor ready for
operational forecasting where the candidate model achieves an average
TSS=0.540.03 and HSS=0.370.07. (ii) we demonstrated that our
full-disk model can learn conspicuous features corresponding to active regions
from full-disk magnetogram images, and (iii) our experimental evaluation
suggests that our model can predict near-limb flares with adept skill and the
predictions are based on relevant active regions (ARs) or AR characteristics
from full-disk magnetograms.Comment: This is a preprint accepted at the 6th International Conference on
Artificial Intelligence and Knowledge Engineering (AIKE), 2023. 8 pages, 6
figure
Testing predictors of eruptivity using parametric flux emergence simulations
Solar flares and coronal mass ejections (CMEs) are among the most energetic
events in the solar system, impacting the near-Earth environment. Flare
productivity is empirically known to be correlated with the size and complexity
of active regions. Several indicators, based on magnetic-field data from active
regions, have been tested for flare forecasting in recent years. None of these
indicators, or combinations thereof, have yet demonstrated an unambiguous
eruption or flare criterion. Furthermore, numerical simulations have been only
barely used to test the predictability of these parameters. In this context, we
used the 3D parametric MHD numerical simulations of the self-consistent
formation of the flux emergence of a twisted flux tube, inducing the formation
of stable and unstable magnetic flux ropes of Leake (2013, 2014). We use these
numerical simulations to investigate the eruptive signatures observable in
various magnetic scalar parameters and provide highlights on data analysis
processing. Time series of 2D photospheric-like magnetograms are used from
parametric simulations of stable and unstable flux emergence, to compute a list
of about 100 different indicators. This list includes parameters previously
used for operational forecasting, physical parameters used for the first time,
as well as new quantities specifically developed for this purpose. Our results
indicate that only parameters measuring the total non-potentiality of active
regions associated with magnetic inversion line properties, such as the
Falconer parameters , , and , as well as the
new current integral and length parameters, present a
significant ability to distinguish the eruptive cases of the model from the
non-eruptive cases, possibly indicating that they are promising flare and
eruption predictors.Comment: 46 pages, 16 figures, accepted for publication in Space Weather and
Space Climate on June, 8t
Solar Magnetic Feature Detection and Tracking for Space Weather Monitoring
We present an automated system for detecting, tracking, and cataloging
emerging active regions throughout their evolution and decay using SOHO
Michelson Doppler Interferometer (MDI) magnetograms. The SolarMonitor Active
Region Tracking (SMART) algorithm relies on consecutive image differencing to
remove both quiet-Sun and transient magnetic features, and region-growing
techniques to group flux concentrations into classifiable features. We
determine magnetic properties such as region size, total flux, flux imbalance,
flux emergence rate, Schrijver's R-value, R* (a modified version of R), and
Falconer's measurement of non-potentiality. A persistence algorithm is used to
associate developed active regions with emerging flux regions in previous
measurements, and to track regions beyond the limb through multiple solar
rotations. We find that the total number and area of magnetic regions on disk
vary with the sunspot cycle. While sunspot numbers are a proxy to the solar
magnetic field, SMART offers a direct diagnostic of the surface magnetic field
and its variation over timescale of hours to years. SMART will form the basis
of the active region extraction and tracking algorithm for the Heliophysics
Integrated Observatory (HELIO)
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