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

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

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

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

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

Représentation des Images via les Maxima en Ondelettes Application à l'extraction des Objets

L'extraction de sources constitue l'une des étapes essentielles de l'analyse des images astronomiques. Dans la présente communication, elle est abordée sous l'angle des maxima locaux de la transformée en ondelettes. L'idée centrale réside dans l'association entre un coefficient en ondelettes et un coefficient d'une fonction d'échelle (pyrel). Cette association résulte d'une interprétation du coefficient en ondelettes basée sur l'ajustement local de l'image avec un profil correspond à une fonction d'échelle superposé à un fond variable. En déterminant les maxima locaux, spatialement et en échelle, de la transformée en ondelettes, on localise ainsi les pyrels qu'on va utiliser pour la reconstruction. Un ajustement des intensités permet de réduire les résidus de la reconstruction. Un processus itératif sur les résidus successifs permet de converger vers une représentation parcimonieuse de l'image. La transformée en ondelettes utilisée pour localiser les pyrels est celle qui provient de l'algorithme à trous. Comme on ne tient compte que des maxima dans la gamme des échelles accessibles, la reconstruction est effectuée à un fond près, tel que sa transformée en ondelettes ne contient aucun coefficient statistiquement significatif. L'ensemble des paramètres des J pyrels détectés, positions, échelles et amplitudes, permet ainsi de reconstruire une image qui ne diffère de l'image originale que de ce fond. On projette ensuite les positions de tous les maxima sur une grille. Un algorithme de croissance de région permet d'attribuer à chaque pixel de cette grille une étiquette, tous les maxima dont les positions appartenant au même domaine connexe ont la même étiquette. Ceci permet d'extraire et de reconstruire les sources correspondantes. Cette nouvelle approche permet ainsi une décomposition très aisée en sources

The Brera Multi-scale Wavelet (BMW) ROSAT HRI source catalog. I: the algorithm

We present a new detection algorithm based on the wavelet transform for the analysis of high energy astronomical images. The wavelet transform, due to its multi-scale structure, is suited for the optimal detection of point-like as well as extended sources, regardless of any loss of resolution with the off-axis angle. Sources are detected as significant enhancements in the wavelet space, after the subtraction of the non-flat components of the background. Detection thresholds are computed through Monte Carlo simulations in order to establish the expected number of spurious sources per field. The source characterization is performed through a multi-source fitting in the wavelet space. The procedure is designed to correctly deal with very crowded fields, allowing for the simultaneous characterization of nearby sources. To obtain a fast and reliable estimate of the source parameters and related errors, we apply a novel decimation technique which, taking into account the correlation properties of the wavelet transform, extracts a subset of almost independent coefficients. We test the performance of this algorithm on synthetic fields, analyzing with particular care the characterization of sources in poor background situations, where the assumption of Gaussian statistics does not hold. For these cases, where standard wavelet algorithms generally provide underestimated errors, we infer errors through a procedure which relies on robust basic statistics. Our algorithm is well suited for the analysis of images taken with the new generation of X-ray instruments equipped with CCD technology which will produce images with very low background and/or high source density.Comment: 8 pages, 6 figures, ApJ in pres

Multiscale image restoration by the À trous algorithm

The Discrete Wavelet Transform can be performed by several algorithms . Among them the "À trous" leads ta an extensive data redundancy . Thus this algorithm is not useful for data compression, but this redundancy can be a useful asset for image restoration, In this paper. we firstly describe the principles of the algorithm and some connected tools. Then we describe an iterative restoration method based on the significant coefficients . The regularization of the inversion is provided by the restriction of the numberofcoefficients . An example is given from the regularization of Richarson-Lucy's iterative deconvolution algorithm.La transformation en ondelettes discrète peut être réalisée par différents algorithmes. Parmi ceux-ci, l'algorithme à trous conduit à une importante redondance de données. Si cette redondance le rend impraticable pour la compression des signaux, elle peut être, au contraire, un atout pour la restauration des images. Dans cet article nous exposerons tout d'abord les fondements de cet algorithme et les divers outils associés (transformation, inversion, visualisation). Nous développerons ensuite une méthode itérative de restauration des images basée sur la notion de coefficients significatifs. La réduction des coefficients conduit à régulariser le probllème inverse lié à la déconvolution. Un exemple est donné en se basant sur l'inversion par la méthode de Richardson-Luc

Multiscale methods applied to the analysis of synthetic aperture radar images

In this paper, we propose a filtering multiscale method to remove the speckle noise in synthetic aperture radar (SAR) images . This filtering is based on the à trous algorithm . It is derived from the multiscale methods developed for astronomical images using the extraction of significant structures . Nevertheless, the multiplicative behaviour of the speckle implies the wavelet thresholding to be modified according to the speckle noise statistic properties . We start with a classical approach based on a logarithmic transform of the image . Then, another method based on the energy of the image is presented . It allows one to obtain a better radiometrical precision in the filtered image . An original analysis is presented that takes advantage of the information given by the significant wavelet coefficients obtained from the thresholding procedure . This analysis is used to show the temporal variations at different scales and to extract the structures at small scales .Cet article propose une méthode de filtrage multiéchelle du bruit de speckle présent dans les images radar à ouverture synthétique. Ce filtrage est basé sur l'utilisation de l'algorithme à trous et s'inspire des méthodes multiéchelle d'extraction des structures significatives développées pour l'imagerie astronomique. Cependant, la nature multiplicative du bruit de speckle conduit à reconsidérer la méthode de seuillage dans l'espace des ondelettes et une première approche basée sur une transformation logarithmique de l'image est présentée. Une seconde approche, s'appuyant sur l'énergie du signal permet d'obtenir des images filtrées ayant une meilleure précision radiométrique. L'information fournie par les coefficients d'ondelettes significatifs est exploitée dans une analyse originale de l'image afin de mettre en évidence les variations temporelles des structures aux différentes échelles, et d'extraire les structures significatives aux petites échelles