2,949 research outputs found
A meta-analysis of the magnetic line broadening in the solar atmosphere
A multi-line Bayesian analysis of the Zeeman broadening in the solar
atmosphere is presented. A hierarchical probabilistic model, based on the
simple but realistic Milne-Eddington approximation to the solution of the
radiative transfer equation, is used to explain the data in the optical and
near infrared. Our method makes use of the full line profiles of a more than
500 spectral lines from 4000 to 1.8 m. Although the problem suffers
from a strong degeneracy between the magnetic broadening and any other
remaining broadening mechanism, the hierarchical model allows to isolate the
magnetic contribution with reliability. We obtain the cumulative distribution
function for the field strength and use it to put reliable upper limits to the
unresolved magnetic field strength in the solar atmosphere. The field is below
160-180 G with 90% probability.Comment: 9 pages, 6 figures, accepted for publication in A&A. Fixed reference
Image Reconstruction with Analytical Point Spread Functions
The image degradation produced by atmospheric turbulence and optical
aberrations is usually alleviated using post-facto image reconstruction
techniques, even when observing with adaptive optics systems. These techniques
rely on the development of the wavefront using Zernike functions and the
non-linear optimization of a certain metric. The resulting optimization
procedure is computationally heavy. Our aim is to alleviate this
computationally burden. To this aim, we generalize the recently developed
extended Zernike-Nijboer theory to carry out the analytical integration of the
Fresnel integral and present a natural basis set for the development of the
point spread function in case the wavefront is described using Zernike
functions. We present a linear expansion of the point spread function in terms
of analytic functions which, additionally, takes defocusing into account in a
natural way. This expansion is used to develop a very fast phase-diversity
reconstruction technique which is demonstrated through some applications. This
suggest that the linear expansion of the point spread function can be applied
to accelerate other reconstruction techniques in use presently and based on
blind deconvolution.Comment: 10 pages, 4 figures, accepted for publication in Astronomy &
Astrophysic
Determination of the cross-field density structuring in coronal waveguides using the damping of transverse waves
Time and spatial damping of transverse magnetohydrodynamic (MHD) kink
oscillations is a source of information on the cross-field variation of the
plasma density in coronal waveguides. We show that a probabilistic approach to
the problem of determining the density structuring from the observed damping of
transverse oscillations enables us to obtain information on the two parameters
that characterise the cross-field density profile. The inference is performed
by computing the marginal posterior distributions for density contrast and
transverse inhomo- geneity length-scale using Bayesian analysis and damping
ratios for transverse oscillations under the assumption that damping is
produced by resonant absorption. The obtained distributions show that, for
damping times of a few oscillatory periods, low density contrasts and short
inho- mogeneity length scales are more plausible in explaining observations.
This means that valuable information on the cross-field density profile can be
obtained even if the inversion problem, with two unknowns and one observable,
is a mathematically ill-posed problem.Comment: 5 pages, 3 figures, accepte
Compressive Sensing for Spectroscopy and Polarimetry
We demonstrate through numerical simulations with real data the feasibility
of using compressive sensing techniques for the acquisition of
spectro-polarimetric data. This allows us to combine the measurement and the
compression process into one consistent framework. Signals are recovered thanks
to a sparse reconstruction scheme from projections of the signal of interest
onto appropriately chosen vectors, typically noise-like vectors. The
compressibility properties of spectral lines are analyzed in detail. The
results shown in this paper demonstrate that, thanks to the compressibility
properties of spectral lines, it is feasible to reconstruct the signals using
only a small fraction of the information that is measured nowadays. We
investigate in depth the quality of the reconstruction as a function of the
amount of data measured and the influence of noise. This change of paradigm
also allows us to define new instrumental strategies and to propose
modifications to existing instruments in order to take advantage of compressive
sensing techniques.Comment: 11 pages, 9 figures, accepted for publication in A&
Bayesian least squares deconvolution
Aims. To develop a fully Bayesian least squares deconvolution (LSD) that can
be applied to the reliable detection of magnetic signals in noise-limited
stellar spectropolarimetric observations using multiline techniques. Methods.
We consider LSD under the Bayesian framework and we introduce a flexible
Gaussian Process (GP) prior for the LSD profile. This prior allows the result
to automatically adapt to the presence of signal. We exploit several linear
algebra identities to accelerate the calculations. The final algorithm can deal
with thousands of spectral lines in a few seconds. Results. We demonstrate the
reliability of the method with synthetic experiments and we apply it to real
spectropolarimetric observations of magnetic stars. We are able to recover the
magnetic signals using a small number of spectral lines, together with the
uncertainty at each velocity bin. This allows the user to consider if the
detected signal is reliable. The code to compute the Bayesian LSD profile is
freely available.Comment: 8 pages, accepted for publication in A&
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
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