346 research outputs found
A Greedy Algorithm for a Sparse Scalet Decomposition
International audienceSparse decompositions were mainly developed to optimize the signal or the image compression. The sparsity was first obtained by a coefficient thresholding. The matching pursuit (MP) algorithms were implemented to extract the optimal patterns from a given dictionary. They carried out a new insight on the sparse representations. In this communication, this way is followed. It takes into account the goal to obtain a sparse multiscale decomposition with the different constraints: i/ to get a sparse representation with patterns looking like to Gaussian functions, ii/ to be able to decompose into patterns with only positive amplitudes, iii/ to get a representation from a translated and dilated pattern, iv/ to constrain the representation by a threshold, v/ to separate the sparse signal from a smooth baseline. Different greedy algorithms were built from the use of redundant wavelet transforms (pyramidal and `a trous ones), for 1D signals and 2D images. Experimentations on astronomical images allow one a gain of about two in sparsity compared to a classical DWT thresholding. A fine denoising is obtained. The results do not display any wavy artifacts. This decomposition is an efficient tool for astronomical image analysis
A new family of non--linear filters for background subtraction of wide--field surveys
In this paper the definitions and the properties of a newle dedicated set of
high-frequency filters based on smoothing-and-clipping are briefly described.
New applications for reduction of wide--field 2048x2048 CCD spectral and direct
images of a new deep survey KISS (KPNO International Spectral Survey) are also
presented. The developed software is available both as a C subroutine and as an
installed MIDAS environment command.Comment: 8 pages with 2 Postscript figures. The text with full figures
obtainable from this http URL
http://193.125.89.73/~akn/cont_with_figures.ps.g
Automated derivation of stellar atmospheric parameters and chemical abundances: the MATISSE algorithm
We present an automated procedure for the derivation of atmospheric
parameters (Teff, log g, [M/H]) and individual chemical abundances from stellar
spectra. The MATrix Inversion for Spectral SythEsis (MATISSE) algorithm
determines a basis, B_\theta(\lambda), allowing to derive a particular stellar
parameter \theta by projection of an observed spectrum. The B_\theta(\lambda)
function is determined from an optimal linear combination of theoretical
spectra and it relates, in a quantitative way, the variations in the spectrum
flux with variations in \theta. An application of this method to the GAIA/RVS
spectral range is described, together with its performances for different types
of stars of various metallicities. Blind tests with synthetic spectra of
randomly selected parameters and observed input spectra are also presented. The
method gives rapid, accurate and stable results and it can be efficiently
applied to the study of stellar populations through the analysis of large
spectral data sets, including moderate to low signal to noise spectra
A multiscale regularized restoration algorithm for XMM-Newton data
We introduce a new multiscale restoration algorithm for images with few
photons counts and its use for denoising XMM data. We use a thresholding of the
wavelet space so as to remove the noise contribution at each scale while
preserving the multiscale information of the signal. Contrary to other
algorithms the signal restoration process is the same whatever the signal to
noise ratio is. Thresholds according to a Poisson noise process are indeed
computed analytically at each scale thanks to the use of the unnormalized Haar
wavelet transform. Promising preliminary results are obtained on X-ray data for
Abell 2163 with the computation of a temperature map.Comment: To appear in the Proceedings of `Galaxy Clusters and the High
Redshift Universe Observed in X-rays', XXIth Moriond Astrophysics Meeting
(March 2001), Eds. Doris Neumann et a
Introduction to the Restoration of Astrophysical Images by Multiscale Transforms and Bayesian Methods
This book is a collection of 19 articles which reflect the courses given at the Collège de France/Summer school “Reconstruction d'images − Applications astrophysiques“ held in Nice and Fréjus, France, from June 18 to 22, 2012. The articles presented in this volume address emerging concepts and methods that are useful in the complex process of improving our knowledge of the celestial objects, including Earth
The AMBRE Project: Stellar Parameterisation of the ESO:UVES archived spectra
The AMBRE Project is a collaboration between the European Southern
Observatory (ESO) and the Observatoire de la Cote d'Azur (OCA) that has been
established in order to carry out the determination of stellar atmospheric
parameters for the archived spectra of four ESO spectrographs.
The analysis of the UVES archived spectra for their stellar parameters has
been completed in the third phase of the AMBRE Project. From the complete
ESO:UVES archive dataset that was received covering the period 2000 to 2010,
51921 spectra for the six standard setups were analysed. The AMBRE analysis
pipeline uses the stellar parameterisation algorithm MATISSE to obtain the
stellar atmospheric parameters. The synthetic grid is currently constrained to
FGKM stars only.
Stellar atmospheric parameters are reported for 12,403 of the 51,921 UVES
archived spectra analysed in AMBRE:UVES. This equates to ~23.9% of the sample
and ~3,708 stars. Effective temperature, surface gravity, metallicity and alpha
element to iron ratio abundances are provided for 10,212 spectra (~19.7%),
while at least effective temperature is provided for the remaining 2,191
spectra. Radial velocities are reported for 36,881 (~71.0%) of the analysed
archive spectra. Typical external errors of sigmaTeff~110dex,
sigmalogg~0.18dex, sigma[M/H]~0.13dex, and sigma[alpha/Fe]~0.05dex with some
reported variation between giants and dwarfs and between setups are reported.
UVES is used to observe an extensive collection of stellar and non-stellar
objects all of which have been included in the archived dataset provided to OCA
by ESO. The AMBRE analysis extracts those objects which lie within the FGKM
parameter space of the AMBRE slow rotating synthetic spectra grid. Thus by
homogeneous blind analysis AMBRE has successfully extracted and parameterised
the targeted FGK stars (23.9% of the analysed sample) from within the ESO:UVES
archive.Comment: 19 pages, 16 figures, 11 table
Parameter Estimation from an Optimal Projection in a Local Environment
The parameter fit from a model grid is limited by our capability to reduce
the number of models, taking into account the number of parameters and the non
linear variation of the models with the parameters. The Local MultiLinear
Regression (LMLR) algorithms allow one to fit linearly the data in a local
environment. The MATISSE algorithm, developed in the context of the estimation
of stellar parameters from the Gaia RVS spectra, is connected to this class of
estimators. A two-steps procedure was introduced. A raw parameter estimation is
first done in order to localize the parameter environment. The parameters are
then estimated by projection on specific vectors computed for an optimal
estimation. The MATISSE method is compared to the estimation using the
objective analysis. In this framework, the kernel choice plays an important
role. The environment needed for the parameter estimation can result from it.
The determination of a first parameter set can be also avoided for this
analysis. These procedures based on a local projection can be fruitfully
applied to non linear parameter estimation if the number of data sets to be
fitted is greater than the number of models
Density estimation with non-parametric methods
One key issue in several astrophysical problems is the evaluation of the
density probability function underlying an observational discrete data set. We
here review two non-parametric density estimators which recently appeared in
the astrophysical literature, namely the adaptive kernel density estimator and
the Maximum Penalized Likelihood technique, and describe another method based
on the wavelet transform.
The efficiency of these estimators is tested by using extensive numerical
simulations in the one-dimensional case. The results are in good agreement with
theoretical functions and the three methods appear to yield consistent
estimates.
In order to check these estimators with respect to previous studies, two
galaxy redshift samples (the galaxy cluster A3526 and the Corona Borealis
region) have been analyzed.Comment: 21 pages, LaTeX2e file with 9 figures and 2 tables (automatically
included) - To appear in Astronomy & Astrophysic
The AMBRE Project: Parameterisation of FGK-type stars from the ESO:HARPS archived spectra
The AMBRE project is a collaboration between the European Southern
Observatory (ESO) and the Observatoire de la Cote d'Azur (OCA). It has been
established to determine the stellar atmospheric parameters (effective
temperature, surface gravity, global metallicities and abundance of
alpha-elements over iron) of the archived spectra of four ESO spectrographs.
The analysis of the ESO:HARPS archived spectra is presented. The sample being
analysed (AMBRE:HARPS) covers the period from 2003 to 2010 and is comprised of
126688 scientific spectra corresponding to 17218 different stars. For the
analysis of the spectral sample, the automated pipeline developed for the
analysis of the AMBRE:FEROS archived spectra has been adapted to the
characteristics of the HARPS spectra. Within the pipeline, the stellar
parameters are determined by the MATISSE algorithm, developed at OCA for the
analysis of large samples of stellar spectra in the framework of galactic
archaeology. In the present application, MATISSE uses the AMBRE grid of
synthetic spectra, which covers FGKM-type stars for a range of gravities and
metallicities. We first determined the radial velocity and its associated error
for the ~15% of the AMBRE:HARPS spectra, for which this velocity had not been
derived by the ESO:HARPS reduction pipeline. The stellar atmospheric parameters
and the associated chemical index [alpha/Fe] with their associated errors have
then been estimated for all the spectra of the AMBRE:HARPS archived sample.
Based on quality criteria, we accepted and delivered the parameterisation of
~71% of the total sample to ESO. These spectra correspond to ~10706 stars; each
are observed between one and several hundred times. This automatic
parameterisation of the AMBRE:HARPS spectra shows that the large majority of
these stars are cool main-sequence dwarfs with metallicities greater than -0.5
dex
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