76 research outputs found
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
Expectation Maximization for Hard X-ray Count Modulation Profiles
This paper is concerned with the image reconstruction problem when the
measured data are solar hard X-ray modulation profiles obtained from the Reuven
Ramaty High Energy Solar Spectroscopic Imager (RHESSI)} instrument. Our goal is
to demonstrate that a statistical iterative method classically applied to the
image deconvolution problem is very effective when utilized for the analysis of
count modulation profiles in solar hard X-ray imaging based on Rotating
Modulation Collimators. The algorithm described in this paper solves the
maximum likelihood problem iteratively and encoding a positivity constraint
into the iterative optimization scheme. The result is therefore a classical
Expectation Maximization method this time applied not to an image deconvolution
problem but to image reconstruction from count modulation profiles. The
technical reason that makes our implementation particularly effective in this
application is the use of a very reliable stopping rule which is able to
regularize the solution providing, at the same time, a very satisfactory
Cash-statistic (C-statistic). The method is applied to both reproduce synthetic
flaring configurations and reconstruct images from experimental data
corresponding to three real events. In this second case, the performance of
Expectation Maximization, when compared to Pixon image reconstruction, shows a
comparable accuracy and a notably reduced computational burden; when compared
to CLEAN, shows a better fidelity with respect to the measurements with a
comparable computational effectiveness. If optimally stopped, Expectation
Maximization represents a very reliable method for image reconstruction in the
RHESSI context when count modulation profiles are used as input data
Multi-scale CLEAN in hard X-ray solar imaging
Multi-scale deconvolution is an ill-posed inverse problem in imaging, with
applications ranging from microscopy, through medical imaging, to astronomical
remote sensing. In the case of high-energy space telescopes, multi-scale
deconvolution algorithms need to account for the peculiar property of native
measurements, which are sparse samples of the Fourier transform of the incoming
radiation. The present paper proposes a multi-scale version of CLEAN, which is
the most popular iterative deconvolution method in Fourier space imaging. Using
synthetic data generated according to a simulated but realistic source
configuration, we show that this multi-scale version of CLEAN performs better
than the original one in terms of accuracy, photometry, and regularization.
Further, the application to a data set measured by the NASA Reuven Ramaty High
Energy Solar Spectroscopic Imager (RHESSI) shows the ability of multi-scale
CLEAN to reconstruct rather complex topographies, characteristic of a real
flaring event
Inverse diffraction for the Atmospheric Imaging Assembly in the Solar Dynamics Observatory
The Atmospheric Imaging Assembly in the Solar Dynamics Observatory provides
full Sun images every 1 seconds in each of 7 Extreme Ultraviolet passbands.
However, for a significant amount of these images, saturation affects their
most intense core, preventing scientists from a full exploitation of their
physical meaning. In this paper we describe a mathematical and automatic
procedure for the recovery of information in the primary saturation region
based on a correlation/inversion analysis of the diffraction pattern associated
to the telescope observations. Further, we suggest an interpolation-based
method for determining the image background that allows the recovery of
information also in the region of secondary saturation (blooming)
Properties of the Acceleration Regions in Several Loop-structured Solar Flares
Using {\em RHESSI} hard X-ray imaging spectroscopy observations, we analyze
electron flux maps for a number of extended coronal loop flares. For each
event, we fit a collisional model with an extended acceleration region to the
observed variation of loop length with electron energy , resulting in
estimates of the plasma density in, and longitudinal extent of, the
acceleration region. These quantities in turn allow inference of the number of
particles within the acceleration region and hence the filling factor --
the ratio of the emitting volume to the volume that encompasses the emitting
region(s). We obtain values of that lie mostly between 0.1 and 1.0; the
(geometric) mean value is , somewhat less than, but
nevertheless consistent with, unity. Further, coupling information on the
number of particles in the acceleration region with information on the total
rate of acceleration of particles above a certain reference energy (obtained
from spatially-integrated hard X-ray data) also allows inference of the
specific acceleration rate (electron s per ambient electron above the
chosen reference energy). We obtain a (geometric) mean value of the specific
acceleration rate keV)
electrons s per ambient electron; this value has implications both for
the global electrodynamics associated with replenishment of the acceleration
region and for the nature of the particle acceleration process
The process of data formation for the Spectrometer/Telescope for Imaging X-rays (STIX) in Solar Orbiter
The Spectrometer/Telescope for Imaging X-rays (STIX) is a hard X-ray imaging
spectroscopy device to be mounted in the Solar Orbiter cluster with the aim of
providing images and spectra of solar flaring regions at different photon
energies in the range from a few keV to around 150 keV. The imaging modality of
this telescope is based on the Moire pattern concept and utilizes 30
sub-collimators, each one containing a pair of co-axial grids. This paper
applies Fourier analysis to provide the first rigorous description of the data
formation process in STIX. Specifically, we show that, under first harmonic
approximation, the integrated counts measured by STIX sub-collimators can be
interpreted as specific spatial Fourier components of the incoming photon flux,
named visibilities. Fourier analysis also allows the quantitative assessment of
the reliability of such interpretation. The description of STIX data in terms
of visibilities has a notable impact on the image reconstruction process, since
it fosters the application of Fourier-based imaging algorithms.Comment: submitted to SIAM Journal on Imaging Science
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