1,598 research outputs found
Stokes Inversion based on Convolutional Neural Networks
Spectropolarimetric inversions are routinely used in the field of Solar
Physics for the extraction of physical information from observations. The
application to two-dimensional fields of view often requires the use of
supercomputers with parallelized inversion codes. Even in this case, the
computing time spent on the process is still very large. Our aim is to develop
a new inversion code based on the application of convolutional neural networks
that can quickly provide a three-dimensional cube of thermodynamical and
magnetic properties from the interpretation of two-dimensional maps of Stokes
profiles. We train two different architectures of fully convolutional neural
networks. To this end, we use the synthetic Stokes profiles obtained from two
snapshots of three-dimensional magneto-hydrodynamic numerical simulations of
different structures of the solar atmosphere. We provide an extensive analysis
of the new inversion technique, showing that it infers the thermodynamical and
magnetic properties with a precision comparable to that of standard inversion
techniques. However, it provides several key improvements: our method is around
one million times faster, it returns a three-dimensional view of the physical
properties of the region of interest in geometrical height, it provides
quantities that cannot be obtained otherwise (pressure and Wilson depression)
and the inferred properties are decontaminated from the blurring effect of
instrumental point spread functions for free. The code is provided for free on
a specific repository, with options for training and evaluation.Comment: 18 pages, 14 figures, accepted for publication in Astronomy &
Astrophysic
Enhancing SDO/HMI images using deep learning
The Helioseismic and Magnetic Imager (HMI) provides continuum images and
magnetograms with a cadence better than one per minute. It has been
continuously observing the Sun 24 hours a day for the past 7 years. The obvious
trade-off between full disk observations and spatial resolution makes HMI not
enough to analyze the smallest-scale events in the solar atmosphere. Our aim is
to develop a new method to enhance HMI data, simultaneously deconvolving and
super-resolving images and magnetograms. The resulting images will mimic
observations with a diffraction-limited telescope twice the diameter of HMI.
Our method, which we call Enhance, is based on two deep fully convolutional
neural networks that input patches of HMI observations and output deconvolved
and super-resolved data. The neural networks are trained on synthetic data
obtained from simulations of the emergence of solar active regions. We have
obtained deconvolved and supper-resolved HMI images. To solve this ill-defined
problem with infinite solutions we have used a neural network approach to add
prior information from the simulations. We test Enhance against Hinode data
that has been degraded to a 28 cm diameter telescope showing very good
consistency. The code is open source.Comment: 13 pages, 10 figures. Accepted for publication in Astronomy &
Astrophysic
Spectropolarimetric analysis of an active region filament. I. Magnetic and dynamical properties from single component inversions
The determination of the magnetic filed vector in solar filaments is possible
by interpreting the Hanle and Zeeman effects in suitable chromospheric spectral
lines like those of the He I multiplet at 10830 A. We study the vector magnetic
field of an active region filament (NOAA 12087). Spectropolarimetric data of
this active region was acquired with the GRIS instrument at the GREGOR
telescope and studied simultaneously in the chromosphere with the He I 10830 A
multiplet and in the photosphere with the Si I 10827 A line. As it is usual
from previous studies, only a single component model is used to infer the
magnetic properties of the filament. The results are put into a solar context
with the help of the Solar Dynamic Observatory images. Some results clearly
point out that a more complex inversion had to be done. Firstly, the Stokes
map of He I does not show any clear signature of the presence of the filament.
Secondly, the local azimuth map follows the same pattern than Stokes as if
the polarity of Stokes were conditioning the inference to very different
magnetic field even with similar linear polarization signals. This indication
suggests that the Stokes could be dominated by the below magnetic field
coming from the active region, and not, from the filament itself. Those and
more evidences will be analyzed in depth and a more complex inversion will be
attempted in the second part of this series.Comment: 18 pages, 19 figures, accepted for publication in A&
Spatial deconvolution of spectropolarimetric data: an application to quiet Sun magnetic elements
Observations of the Sun from the Earth are always limited by the presence of
the atmosphere, which strongly disturbs the images. A solution to this problem
is to place the telescopes in space satellites, which produce observations
without any (or limited) atmospheric aberrations. However, even though the
images from space are not affected by atmospheric seeing, the optical
properties of the instruments still limit the observations. In the case of
diffraction limited observations, the PSF establishes the maximum allowed
spatial resolution, defined as the distance between two nearby structures that
can be properly distinguished. In addition, the shape of the PSF induce a
dispersion of the light from different parts of the image, leading to what is
commonly termed as stray light or dispersed light. This effect produces that
light observed in a spatial location at the focal plane is a combination of the
light emitted in the object at relatively distant spatial locations. We aim to
correct the effect produced by the telescope's PSF using a deconvolution
method, and we decided to apply the code on Hinode/SP quiet Sun observations.
We analyze the validity of the deconvolution process with noisy data and we
infer the physical properties of quiet Sun magnetic elements after the
deconvolution process.Comment: 14 pages, 9 figure
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