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Register a new userThe INT Search for Metal-Poor Stars. Spectroscopic Observations and Classification via Artificial Neural Networks
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Carlos Allende Prieto
Publication date
2000
fields
Physics
and research's language is
English
Authors
C. Allende Prieto
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With the dual aims of enlarging the list of extremely metal-poor stars identified in the Galaxy, and boosting the numbers of moderately metal-deficient stars in directions that sample the rotational properties of the thick disk, we have used the 2.5m Isaac Newton Telescope and the Intermediate Dispersion Spectrograph to carry out a survey of brighter (primarily northern hemisphere) metal-poor candidates selected from the HK objective-prism/interference-filter survey of Beers and collaborators. Over the course of only three observing runs (15 nights) we have obtained medium-resolution (resolving power ~ 2000) spectra for 1203 objects (V ~ 11-15). Spectral absorption-line indices and radial velocities have been measured for all of the candidates. Metallicities, quantified by [Fe/H], and intrinsic (B-V)o colors have been estimated for 731 stars with effective temperatures cooler than roughly 6500 K, making use of artificial neural networks (ANNs), trained with spectral indices. We show that this method performs as well as a previously explored Ca II K calibration technique, yet it presents some practical advantages. Among the candidates in our sample, we identify 195 stars with [Fe/H] <= -1.0, 67 stars with [Fe/H] <= -2.0, and 12 new stars with [Fe/H] <= -3.0. Although the EFECTIVE YIELD of metal-poor stars in our sample is not as large as previous HK survey follow-up programs, the rate of discovery per unit of telescope time is quite high.
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Identification of metal-poor stars among field stars is extremely useful for studying the structure and evolution of the Galaxy and of external galaxies. We search for metal-poor stars using the artificial neural network (ANN) and extend its usage to determine absolute magnitudes. We have constructed a library of 167 medium-resolution stellar spectra (R ~ 1200) covering the stellar temperature range of 4200 to 8000 K, log g range of 0.5 to 5.0, and [Fe/H] range of -3.0 to +0.3 dex. This empirical spectral library was used to train ANNs, yielding an accuracy of 0.3 dex in [Fe/H], 200 K in temperature, and 0.3 dex in log g. We found that the independent calibrations of near-solar metallicity stars and metal-poor stars decreases the errors in T_eff and log g by nearly a factor of two. We calculated T_eff, log g, and [Fe/H] on a consistent scale for a large number of field stars and candidate metal-poor stars. We extended the application of this method to the calibration of absolute magnitudes using nearby stars with well-estimated parallaxes. A better calibration accuracy for M_V could be obtained by training separate ANNs for cool, warm, and metal-poor stars. The current accuracy of M_V calibration is (+-)0.3 mag. A list of newly identified metal-poor stars is presented. The M_V calibration procedure developed here is reddening-independent and hence may serve as a powerful tool in studying galactic structure.
Convective line asymmetries in the optical spectrum of two metal-poor stars, Gmb1830 and HD140283, are compared to those observed for solar metallicity stars. The line bisectors of the most metal-poor star, the subgiant HD140283, show a significantly larger velocity span that the expectations for a solar-metallicity star of the same spectral type and luminosity class. The enhanced line asymmetries are interpreted as the signature of the lower metal content, and therefore opacity, in the convective photospheric patterns. These findings point out the importance of three-dimensional convective velocity fields in the interpretation of the observed line asymmetries in metal-poor stars, and in particular, urge for caution when deriving isotopic ratios from observed line shapes and shifts using one-dimensional model atmospheres. The mean line bisector of the photospheric atomic lines is compared with those measured for the strong Mg I b1 and b2 features. The upper part of the bisectors are similar, and assuming they overlap, the bottom end of the stronger lines, which are formed higher in the atmosphere, goes much further to the red. This is in agreement with the expected decreasing of the convective blue-shifts in upper atmospheric layers, and compatible with the high velocity redshifts observed in the chromosphere, transition region, and corona of late-type stars.
The Pristine survey is a narrow-band, photometric survey focused around the wavelength region of the Ca II H & K absorption lines, designed to efficiently search for extremely metal-poor stars. In this work, we use the first results of a medium-resolution spectroscopic follow-up to refine the selection criteria for finding extremely metal-poor stars ($textrm{[Fe/H]} leq -3.0$) in the Pristine survey. We consider methods by which stars can be selected from available broad-band and infrared photometry plus the additional Pristine narrow-band photometry. The spectroscopic sample presented in this paper consists of 205 stars in the magnitude range $14 < V < 18$. Applying the photometric selection criteria cuts the sample down to 149 stars, and from these we report a success rate of 70% for finding stars with $textrm{[Fe/H]} leq -2.5$ and 22% for finding stars with $textrm{[Fe/H]} leq -3.0$. These statistics compare favourably with other surveys that search for extremely metal-poor stars, namely an improvement by a factor of $sim 4-5$ for recovering stars with $textrm{[Fe/H]} leq -3.0$. In addition, Pristine covers a fainter magnitude range than its predecessors, and can thus probe deeper into the Galactic halo.
We present and discuss the results of a search for extremely metal-poor stars based on photometry from data release DR1.1 of the SkyMapper imaging survey of the southern sky. In particular, we outline our photometric selection procedures and describe the low-resolution ($R$ $approx$ 3000) spectroscopic follow-up observations that are used to provide estimates of effective temperature, surface gravity and metallicity ([Fe/H]) for the candidates. The selection process is very efficient: of the 2618 candidates with low-resolution spectra that have photometric metallicity estimates less than or equal to -2.0, 41% have [Fe/H] $leq$ -2.75 and only $sim$7% have [Fe/H] $>$ -2.0 dex. The most metal-poor candidate in the sample has [Fe/H] $<$ -4.75 and is notably carbon-rich. Except at the lowest metallicities ([Fe/H] $<$ -4), the stars observed spectroscopically are dominated by a `carbon-normal population with [C/Fe]$_{1D,LTE}$ $leq$ +1 dex. Consideration of the A(C)$_{1D, LTE}$ versus [Fe/H]$_{1D, LTE}$ diagram suggests that the current selection process is strongly biased against stars with A(C)$_{1D, LTE}$ $>$ 7.3 (predominantly CEMP-$s$) while any bias against stars with A(C)$_{1D, LTE}$ $<$ 7.3 and [C/Fe]$_{LTE}$ $>$ +1 (predominantly CEMP-no) is not readily quantifiable given the uncertainty in the SkyMapper $v$-band DR1.1 photometry. We find that the metallicity distribution function of the observed sample has a power-law slope of $Delta$(Log N)/$Delta$[Fe/H] = 1.5 $pm$ 0.1 dex per dex for -4.0 $leq$ [Fe/H] $leq$ -2.75, but appears to drop abruptly at [Fe/H] $approx$ -4.2, in line with previous studies.
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.
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