3,820 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
The NWRA Classification Infrastructure: Description and Extension to the Discriminant Analysis Flare Forecasting System (DAFFS)
A classification infrastructure built upon Discriminant Analysis has been
developed at NorthWest Research Associates for examining the statistical
differences between samples of two known populations. Originating to examine
the physical differences between flare-quiet and flare-imminent solar active
regions, we describe herein some details of the infrastructure including:
parametrization of large datasets, schemes for handling "null" and "bad" data
in multi-parameter analysis, application of non-parametric multi-dimensional
Discriminant Analysis, an extension through Bayes' theorem to probabilistic
classification, and methods invoked for evaluating classifier success. The
classifier infrastructure is applicable to a wide range of scientific questions
in solar physics. We demonstrate its application to the question of
distinguishing flare-imminent from flare-quiet solar active regions, updating
results from the original publications that were based on different data and
much smaller sample sizes. Finally, as a demonstration of "Research to
Operations" efforts in the space-weather forecasting context, we present the
Discriminant Analysis Flare Forecasting System (DAFFS), a near-real-time
operationally-running solar flare forecasting tool that was developed from the
research-directed infrastructure.Comment: J. Space Weather Space Climate: Accepted / in press; access
supplementary materials through journal; some figures are less than full
resolution for arXi
The Challenge of Machine Learning in Space Weather Nowcasting and Forecasting
The numerous recent breakthroughs in machine learning (ML) make imperative to
carefully ponder how the scientific community can benefit from a technology
that, although not necessarily new, is today living its golden age. This Grand
Challenge review paper is focused on the present and future role of machine
learning in space weather. The purpose is twofold. On one hand, we will discuss
previous works that use ML for space weather forecasting, focusing in
particular on the few areas that have seen most activity: the forecasting of
geomagnetic indices, of relativistic electrons at geosynchronous orbits, of
solar flares occurrence, of coronal mass ejection propagation time, and of
solar wind speed. On the other hand, this paper serves as a gentle introduction
to the field of machine learning tailored to the space weather community and as
a pointer to a number of open challenges that we believe the community should
undertake in the next decade. The recurring themes throughout the review are
the need to shift our forecasting paradigm to a probabilistic approach focused
on the reliable assessment of uncertainties, and the combination of
physics-based and machine learning approaches, known as gray-box.Comment: under revie
A hybrid supervised/unsupervised machine learning approach to solar flare prediction
We introduce a hybrid approach to solar flare prediction, whereby a
supervised regularization method is used to realize feature importance and an
unsupervised clustering method is used to realize the binary flare/no-flare
decision. The approach is validated against NOAA SWPC data
Turbulence, Complexity, and Solar Flares
The issue of predicting solar flares is one of the most fundamental in
physics, addressing issues of plasma physics, high-energy physics, and
modelling of complex systems. It also poses societal consequences, with our
ever-increasing need for accurate space weather forecasts. Solar flares arise
naturally as a competition between an input (flux emergence and rearrangement)
in the photosphere and an output (electrical current build up and resistive
dissipation) in the corona. Although initially localised, this redistribution
affects neighbouring regions and an avalanche occurs resulting in large scale
eruptions of plasma, particles, and magnetic field. As flares are powered from
the stressed field rooted in the photosphere, a study of the photospheric
magnetic complexity can be used to both predict activity and understand the
physics of the magnetic field. The magnetic energy spectrum and multifractal
spectrum are highlighted as two possible approaches to this.Comment: 2 figure
- …