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Register a new user(Teff,log g,[Fe/H]) Classification of Low-Resolution Stellar Spectra using Artificial Neural Networks
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Added by
Carlos Allende Prieto
Publication date
1999
fields
Physics
and research's language is
English
Authors
Shawn Snider
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New generation large-aperture telescopes, multi-object spectrographs, and large format detectors are making it possible to acquire very large samples of stellar spectra rapidly. In this context, traditional star-by-star spectroscopic analysis are no longer practical. New tools are required that are capable of extracting quickly and with reasonable accuracy important basic stellar parameters coded in the spectra. Recent analyses of Artificial Neural Networks (ANNs) applied to the classification of astronomical spectra have demonstrated the ability of this concept to derive estimates of temperature and luminosity. We have adapted the back-propagation ANN technique developed by von Hippel et al. (1994) to predict effective temperatures, gravities and overall metallicities from spectra with resolving power ~ 2000 and low signal-to-noise ratio. We show that ANN techniques are very effective in executing a three-parameter (Teff,log g,[Fe/H]) stellar classification. The preliminary results show that the technique is even capable of identifying outliers from the training sample.
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A library of 211 echelle spectra taken with ELODIE at the Observatoire de Haute-Provence is presented. It provides a set of spectroscopic standards covering the full range of gravities and metallicities in the effective temperature interval [4000 K, 6300 K]. The spectra are straightened, wavelength calibrated, cleaned of cosmic ray hits, bad pixels and telluric lines. They cover the spectral range [440 nm, 680 nm] with an instrumental resolution of 42000. For each star, basic data were compiled from the Hipparcos catalogue and the Hipparcos Input Catalogue. Radial velocities with a precision better than 100 m/s are given. Atmospheric parameters (Teff, log g, [Fe/H]) from the literature are discussed. Because of scattered determinations in the bibliography, even for the most well-known stars, these parameters were adjusted by an iterative process which takes account of common or different spectral features between the standards, using our homogeneous set of spectra. Revised values of (Teff, log g, [Fe/H]) are proposed. They are still consistent with the literature, and also lead to the self-consistency of the library, in the sense that similar spectra have similar atmospheric parameters. This adjustment was performed by using step by step a method based on the least square comparison of carefully prepared spectra, which was originally developed for the on-line estimation of the atmospheric parameters of faint field stars (companion paper in the main journal). The spectra and corresponding data will only be available in electronic form at the CDS (ftp cdsarc.u-strasbg.fr or http://cdsweb.u-strasbg.fr/Abstract.html).
We explore the application of artificial neural networks (ANNs) for the estimation of atmospheric parameters (Teff, logg, and [Fe/H]) for Galactic F- and G-type stars. The ANNs are fed with medium-resolution (~ 1-2 A) non flux-calibrated spectroscopic observations. From a sample of 279 stars with previous high-resolution determinations of metallicity, and a set of (external) estimates of temperature and surface gravity, our ANNs are able to predict Teff with an accuracy of ~ 135-150 K over the range 4250 <= Teff <= 6500 K, logg with an accuracy of ~ 0.25-0.30 dex over the range 1.0 <= logg <= 5.0 dex, and [Fe/H] with an accuracy ~ 0.15-0.20 dex over the range -4.0 <= [Fe/H] <= +0.3. Such accuracies are competitive with the results obtained by fine analysis of high-resolution spectra. It is noteworthy that the ANNs are able to obtain these results without consideration of photometric information for these stars. We have also explored the impact of the signal-to-noise ratio (S/N) on the behavior of ANNs, and conclude that, when analyzed with ANNs trained on spectra of commensurate S/N, it is possible to extract physical parameter estimates of similar accuracy with stellar spectra having S/N as low as 13. Taken together, these results indicate that the ANN approach should be of primary importance for use in present and future large-scale spectroscopic surveys.
We present an investigation of velocity fields in early to late M-type hydrodynamic stellar atmosphere models. These velocities will be expressed in classical terms of micro- and macro-turbulent velocities for usage in 1D spectral synthesis. The M-star model parameters range between log g of 3.0 - 5.0 and Teff of 2500 K - 4000 K. We characterize the Teff- and log g-dependence of the hydrodynamical velocity fields in these models with a binning method, and for the determination of micro-turbulent velocities, the Curve of Growth method is used. The macro-turbulent velocities are obtained by convolutions with Gaussian profiles. Velocity fields in M-stars strongly depend on log g and Teff. Their velocity amplitudes increase with decreasing log g and increasing Teff. The 3D hydrodynamical and 1D macro-turbulent velocities range from ~100 m/s for cool high gravity models to ~ 800 m/s - 1000 m/s for hot models or models with low log g. The micro-turbulent velocities range in the order of ~100 m/s for cool models, to ~600 m/s for hot or low log g models. Our M-star structure models are calculated with the 3D radiative-hydrodynamics (RHD) code CO5BOLD. The spectral synthesis on these models is performed with the line synthesis code LINFOR3D.
Aims. In this work we develop a technique to obtain high precision determinations of both metallicity and effective temperature of M dwarfs in the optical. Methods. A new method is presented that makes use of the information of 4104 lines in the 530-690 nm spectral region. It consists in the measurement of pseudo equivalent widths and their correlation with established scales of [Fe/H] and $T_{eff}$. Results. Our technique achieves a $rms$ of 0.08$pm$0.01 for [Fe/H], 91$pm$13 K for $T_{eff}$, and is valid in the (-0.85, 0.26 dex), (2800, 4100 K), and (M0.0, M5.0) intervals for [Fe/H], $T_{eff}$ and spectral type respectively. We also calculated the RMSE$_{V}$ which estimates uncertainties of the order of 0.12 dex for the metallicity and of 293 K for the effective temperature. The technique has an activity limit and should only be used for stars with $log{L_{H_{alpha}}/L_{bol}} < -4.0$. Our method is available online at url{http://www.astro.up.pt/resources/mcal}.
An update on recent methods for automated stellar parametrization is given. We present preliminary results of the ongoing program for rapid parametrization of field stars using medium resolution spectra obtained using Vainu Bappu Telescope at VBO, Kavalur, India. We have used Artificial Neural Network for estimating temperature, gravity, metallicity and absolute magnitude of the field stars. The network for each parameter is trained independently using a large number of calibrating stars. The trained network is used for estimating atmospheric parameters of unexplored field stars.
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