相关论文: An autoencoder of stellar spectra and its applicat…

We describe a scheme to extract linearly supporting (LSU) features from stellar spectra to automatically estimate the atmospheric parameters $T_{eff}$, log$~g$, and [Fe/H]. "Linearly supporting" means that the atmospheric parameters can be…

We present techniques for the estimation of stellar atmospheric parameters (Teff,logg,[Fe/H]) for stars from the SDSS/SEGUE survey. The atmospheric parameters are derived from the observed medium-resolution (R=2000) stellar spectra using…

A scheme for estimating atmospheric parameters T$_{eff}$, log$~g$, and [Fe/H] is proposed on the basis of Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and Haar wavelet. The proposed scheme consists of three processes. A…

Large-scale and deep sky survey missions are rapidly collecting a large amount of stellar spectra, which necessitate the estimation of atmospheric parameters directly from spectra and makes it feasible to statistically investigate latent…

The growth of sky surveys and the large amount of stellar spectra in the current databases, has generated the necessity of developing new methods to estimate atmospheric parameters, a fundamental task on stellar research. In this work we…

Analyses of stellar spectra often begin with the determination of a number of parameters that define a model atmosphere. This work presents a prototype for an automated spectral classification system that uses a 15 nm-wide region around…

Innovation in the ground and space-based instruments has taken us into a new age of spectroscopy, in which a large amount of stellar content is becoming available. So, automatic classification of stellar spectra became subjective in recent…

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,…

This work investigates the spectrum parameterization problem using deep neural networks (DNNs). The proposed scheme consists of the following procedures: first, the configuration of a DNN is initialized using a series of autoencoder neural…

A method is developed for fitting theoretically predicted astronomical spectra to an observed spectrum. Using a hierarchical Bayesian principle, the method takes both systematic and statistical measurement errors into account, which has not…

Libraries of stellar spectra, such as ELODIE (Prugniel & Soubiran 2001), CFLIB (Valdes et al. 2004), or MILES (S\'anchez-Bl\'azquez et al. 2006), are used for a variety of applications, and especially in modelling stellar populations (e. g.…

We applied machine learning to the entire data history of ESO's High Accuracy Radial Velocity Planet Searcher (HARPS) instrument. Our primary goal was to recover the physical properties of the observed objects, with a secondary emphasis on…

With the large amounts of spectroscopic data available today and the very large surveys to come (e.g. Gaia), the need for automatic data analysis software is unquestionable. We thus developed an automatic spectra analysis program for the…

We describe the Zonal Atmospheric Stellar Parameters Estimator (ZASPE), a new algorithm, and its associated code, for determining precise stellar atmospheric parameters and their uncertainties from high resolution echelle spectra of…

A fundamental challenge for wide-field imaging surveys is obtaining follow-up spectroscopic observations: there are > $10^9$ photometrically cataloged sources, yet modern spectroscopic surveys are limited to ~few x $10^6$ targets. As we…

All-sky observations of the Milky Way show both Galactic and non-Galactic diffuse emission, for example from interstellar matter or the cosmic microwave background (CMB). The different emitters are partly superimposed in the measurements,…

In the era of vast spectroscopic surveys focusing on Galactic stellar populations, astronomers want to exploit the large quantity and good quality of data to derive their atmospheric parameters without losing precision from automatic…

This paper investigates the problem of prediction of stellar parameters, based on the star's electromagnetic spectrum. The knowledge of these parameters permits to infer on the evolutionary state of the star. From a statistical point of…

Context: StePar is an automatic code written in Python 3.X designed to compute the stellar atmospheric parameters Teff, log(g), [Fe/H], and of FGK-type stars by means of the EW method. This code has already been extensively tested in…

We describe a self-consistent spectrum analysis technique employing non-LTE line formation, which allows precise atmospheric parameters of massive stars to be derived: 1sigma-uncertainties as low as ~1% in effective temperature and…