The atmospheric parameters and spectral interpolator for the stars of MILES

The atmospheric parameters and spectral interpolator for the stars of MILES

Key Words.:
atlases – stars: abundances – atmospheres – fundamental parameters
4

Abstract

Context: Empirical libraries of stellar spectra are used for stellar classification and synthesis of stellar populations. MILES is a medium spectral-resolution library in the optical domain covering a wide range of temperatures, surface gravities and metallicities.

Aims: We re-determine the atmospheric parameters of these stars in order to improve the homogeneity and accuracy. We build an interpolating function that returns a spectrum as a function of the three atmospheric parameters, and finally, we characterize the precision of the wavelength calibration and stability of the spectral resolution.

Methods: We use the ULySS program with the ELODIE library as a reference and compare the results with literature compilations.

Results: We obtain precisions of 60 K, 0.13 and 0.05 dex respectively for , log g and [Fe/H] for the FGK stars. For the M stars, the mean errors are 38 K, 0.26 and 0.12 dex, and for the OBA 3.5%, 0.17 and 0.13 dex. We construct an interpolator that we test against the MILES stars themselves. We test it also by measuring the atmospheric parameters of the CFLIB stars with MILES as reference and find it to be more reliable than the ELODIE interpolator for the evolved hot stars, like in particular those of the blue horizontal branch.

Conclusions:

1 Introduction

MILES (Sánchez-Blázquez et al. 2006) is a medium resolution library of observed stellar spectra in the optical domain. It is comparable to CFLIB (Valdes et al. 2004) and ELODIE (Prugniel & Soubiran 2001), and is of a particular interest for its accurate flux calibration. The three libraries contain normal stars with a wide range of characteristics, from spectral types O to M, all luminosity classes and a wide distribution of metallicities ( [Fe/H] ) dex.

The empirical libraries have important applications in different fields. They are used as references for stellar classification and determination of atmospheric parameters (see Wu et al. 2011, and references therein). They are also important ingredients for the synthesis of stellar populations, used to study the history of galaxies (Prugniel et al. 2007a). The most important characteristics of a library are (i) the wavelength range, (ii) the spectral resolution and (iii) the distribution of the stars in the parameters’ space whose axes are the effective temperature, , the logarithm of the surface gravity, log g, and the metallicity, [Fe/H]. Other properties, like the precision and uniformity of the wavelength calibration and spectral resolution, or the accuracy of the flux calibration, are also to be considered.

The ELODIE library has been upgraded three times after its publication (Prugniel & Soubiran 2001, 2004; Prugniel et al. 2007b). The last version, ELODIE 3.2 is preliminary described in Wu et al. (2011). It counts 1962 spectra of 1388 stars observed with the eponym echelle spectrograph (Baranne et al. 1996) at the spectral resolution Å (R=), in the wavelength range 3900 to 6800 Å. CFLIB, also known as the “Indo-US” library, has 1273 stars at a resolution5 1.4 Å ( R ) in the range 3460 to 9464 Å. The atmospheric parameters of CFLIB were homogeneously determined by Wu et al. (2011). MILES contains 985 stars at a resolution6 Å in the range 3536 to 7410 Å. The atmospheric parameters of these stars were compiled from the literature or derived from photometric calibrations by Cenarro et al. (2007). The [Mg/Fe] relative abundances were recently determined by de Castro Milone et al. (2011).

The goals of this article are to (i) re-determine homogeneously the atmospheric parameters of the stars of MILES using ELODIE as reference, (ii) characterize the resolution and accuracy of the wavelength calibration and (iii) build an interpolator. This latter is a function, based on an interpolation over all the stars of the library, that returns a spectrum for a given set of atmospheric parameters , , log , and [Fe/H].

In Sect. 2, we describe the steps of the data analysis. In Sect. 3, we present the results and assess their reliability, and Sect. 4 gives the conclusions.

2 Analysis

In this section, we give the details of our analysis. First, we describe the different steps, and then we present in details the determination of the atmospheric parameters and line-spread function and the computation of the interpolator.

2.1 Strategy

To determine the atmospheric parameters, we compare the observed MILES spectra with templates built from the ELODIE library. The minimization, performed with the ULySS program7 (Koleva et al. 2009), is made as described in Wu et al. (2011). Shortly, the underlying model is

(1)

where is the observed spectrum sampled in log, a series of Legendre polynomials of degree n, and a Gaussian broadening function parameterized by the residual velocity , and the dispersion . The TGM function models a stellar spectrum for given atmospheric parameters. It interpolates the ELODIE 3.2 library described in Sect. 2.2. The program minimizes the squared difference between the observations and the model. The free parameters are the three of TGM, the two of G and the n coefficients of .

A single minimization provides the atmospheric parameters and the broadening. The advantage of this simultaneity is to reduce the effects of the degeneracy between the broadening and the atmospheric parameters (see Wu et al. 2011).

The function G encompasses both the effects of the finite spectral resolution and of the physical broadening of the observation and model. The physical broadening is essentially due to rotation and turbulence. The spectral resolution is represented by the so-called line-spread function (LSF), and in first approximation we can write:

(2)

where is the relative physical broadening between the observation and the model (i. e. mismatch of rotation and turbulence) and the relative LSF. The absolute LSF of the observed spectrum is , where is the LSF of the model. The approximations are that (i) neither nor are strictly Gaussians, and (ii) the LSF generally depends on the wavelength, hence we cannot rigorously write convolutions. The Gaussian approximation is certainly acceptable in the present context of moderate spectral resolution because: (i) The physical broadening can often be neglected or can otherwise be assumed Gaussian. (ii) The MILES spectra were acquired with a relatively narrow slit, thus the top-hat signature of the slit is dominated by the intrinsic broadening due to the disperser.

The variation of the LSF with the wavelength has only minor consequences on the atmospheric parameters (see Wu et al. 2011), but we will explain below how we determine it and inject it in TGM to get the most accurate parameters.

In Eq. 1, the role of the multiplicative polynomial, , is to absorb the mismatch of the shape of the continuum, due to uncertainties in the flux calibration. It does not bias the measured atmospheric parameters, because it is included in the fitted model rather than determined in a preliminary normalization. In principle, a moderate degree, n , is sufficient, but a higher degree suppresses the ‘waves’ in the residuals and helps the interpretation of the misfits (the residuals are smaller and it is easier to detect poorly fitted lines). Large values of n, up to 100 or more, do not affect the parameters (Wu et al. 2011). The optimal choice of n depends on the resolution, wavelength range and accuracy of the wavelength calibration. We determined it following the precepts of Koleva et al. (2009). We chose stars of various spectral types, and tested different values in order to locate the plateau where the atmospheric parameters are not sensitive to n. We adopted n .

The choice of ELODIE as reference limits the wavelength range where the spectra can be analysed. In particular, the blue end, below the H & K lines, is unfortunately not used. An alternative would have been to use a theoretical library, like the one of Coelho et al. (2005). We tried this solution, but we found that the misfits are significantly larger than with ELODIE (see Sect. 3.1) and we decided to maintain our initial choice.

In order to handle the wavelength dependence of the LSF, the analysis proceeds in three steps:

Determination of the LSF.

We determine the wavelength-dependent LSF of each spectrum of stars in common between the MILES and ELODIE libraries. We use the uly_lsf command, as described in Sect. 2.3.

Determination of the atmospheric parameters.

We inject the wavelength-dependent relative LSF into the models so that the result has the same resolution characteristics as the observations, and determine the atmospheric parameters calling ulyss.

Construction of the spectral interpolator.

Finally, using these atmospheric parameters, we compute an interpolator. For each wavelength element, a polynomial in , log , and [Fe/H] is adjusted on all the library stars, in order to be used as an interpolating function. This process is introduced in Sect. 2.5.

2.2 ELODIE 3.2: library and interpolator

ELODIE 3.2 is based on the same set of stars as ELODIE 3.1 (Prugniel et al. 2007b) and benefited from several improvements concerning various details of the data reduction, like in particular a better correction of the diffuse light. We note also that a systematic error of 0.0333 Å (i. e. approximately 2 km s) on the wavelengths of the previous version has been corrected (it was due to a bug in the computation of the world coordinate system after a rebinning; ELODIE 3.1 was red-shifted).

The ELODIE interpolator approximates each spectral bin with polynomials in , log g and [Fe/H]. Three different sets of polynomials are defined for the OBA, FGK and M type temperature ranges, and are linearly interpolated in overlapping regions. This interpolator has been noticeably upgraded in the last version, taking into account the stellar rotation and adding some theoretical spectra to extend its range of validity to regions of the parameters’ space scarcely populated of devoid of library stars. The ELODIE 3.2 interpolator is publicly available at http://ulyss.univ-lyon1.fr/models.html, and additional details are given in Wu et al. (2011). A similar interpolator is described in Sect. 2.5 for MILES, with the only difference that the rotation terms are omitted.

2.3 Accurate line-spread function

The LSF describes the instrumental broadening, and may vary with the wavelength. We determined the wavelength dependent broadening by fitting the spectra of the 303 MILES stars belonging also to ELODIE (note that since ELODIE contains repeated observations of the same stars, this corresponds to 404 comparisons). These fits were performed with the function uly_lsf in a series of 400 Å intervals separated by 300 Å, therefore overlapping by 100 Å on both ends. This procedure gives nine sampling points along the ELODIE range.

The change of broadening with wavelength is a consequence of the characteristics of the disperser and design of the spectrograph, but the shift of these functions with respect to the rest-frame wavelengths shall ideally be null. However, the finite precision of the wavelength calibration and uncertain knowledge of the heliocentric velocity of the stars result in residual shifts that may be wavelength dependent. Flexures in the spectrographs or temperature drifts may cause these effects. Their magnitudes are expected to be small fractions of pixels. Sánchez-Blázquez et al. (2006) estimated the precision of their calibration to about 6 km s.These residuals are likely to cancel each others when we average the LSF for all the stars.

We estimated the mean instrumental velocity dispersion and residual shift in each spectral chunk as a clipped average of the individual ones using the IDL procedure biweight_mean that does a bisquare weighting (a median estimation gives identical results).

Absolute LSF

Our analysis is providing the relative LSF between MILES and ELODIE. Since the characterization of the LSF has an intrinsic interest, we will give the absolute LSF obtained deconvolving by the LSF of ELODIE.

The FWHM resolution of the ELODIE spectrograph, measured on the Thorium lines of calibrating spectra varies from 7.0 km s in the blue to 7.4 in the red (Baranne et al. 1996), or respectively 0.09 and 0.17 Å. This corresponds to a mean resolving power of R. The low-resolution (i. e. R  10 000) version of ELODIE 3.2, used in this paper, was produced by convolving the full-resolution spectra with a Gaussian of FWHM = 0.556 Å. Therefore, the final resolution varies from 0.564 to 0.581 Å along the wavelength range, for an average of 0.573 Å.

To check this value, we analysed the LSF of the ELODIE interpolated spectra, having the atmospheric parameters of the MILES stars, using Coelho et al. (2005) as reference. We found a relative broadening of Å, independently of the wavelength. The difference with the value above is surely compatible with the residual rotational broadening of the interpolated spectra, and we adopt the mean value derived above.

The Gaussian width of absolute LSF of MILES is therefore the quadratic sum between the width of the LSF relative to ELODIE and the width of the absolute LSF of ELODIE.

Biased LSF

If the effective spectral resolution was the same for all the spectra of MILES, we could simply inject the LSF into the model and adjust only the atmospheric parameters (i. e. omit the convolution in Eq. 1). However, because of the rotational broadening and dispersion of the instrumental broadening, the effective resolution varies, and we still need to fit the atmospheric parameters and the broadening.

In practice, the model must have a higher spectral resolution than the observation, because it is convolved with G during the analysis (Eq. 1). If we would inject the relative LSF in the model, the result would be broader than the best resolved library spectra. To avoid this difficulty we bias the LSF by subtracting quadratically 40 km s(at any wavelengths) from the width of the mean relative LSF. The resolution of this biased LSF is higher than any spectrum of the library, and it has the correct wavelength dependence.

2.4 Determination of the atmospheric parameters

We fitted the spectra using the ELODIE 3.2 interpolator, injecting the biased LSF previously derived, and assuming a uniform broadening, as described in Eq. 1. In order to avoid trapping in local minimal, we used a grid of initial guesses sampling all the parameters’ space. The nodes of this grid are:

  •  K

  • log g  cm s

  • [Fe/H] 

For the stars belonging to clusters, we adopted and fixed the metallicity to the value given in Cenarro et al. (2007).

The spectra were rebinned into an array of logarithmically spaced wavelengths, each pixel corresponding to 30 km s. This choice oversamples the original spectrum by a factor two in the blue and by 20% in the red. We performed the fit in the region 4200 – 6800 Å, excluding the blue end of the spectra, where the signal-to-noise ratio is lower.

Because the noise estimation in the MILES spectra is not available, we assumed a constant noise, resulting in a uniform weighting of each wavelength bin. We estimated an upper limit to the internal errors on the derived parameters by assuming .

This first minimization localizes the region of the solution, and we refine our measurements running again ulyss with the /clean option to identify and discard the spikes in the signal. They result from the imperfect subtraction of sky lines, removal of spikes due to hits of cosmic rays or stellar emission lines. The second set of derived parameters is very close to the first one, because the MILES spectra were already corrected for most of the observational artifacts.

Finally, the resulting parameters were compared with Cenarro et al. (2007) and the significant outliers were examined by checking the quality of the fit and searching the literature for accurate measurements from high resolution spectroscopy.

2.5 MILES library interpolator

The goal is to build an interpolator similar to the one of the ELODIE library. It may then be used to (i) analyse stellar spectra, for example with ULySS, or (ii) create stellar population models, for example with PEGASE.HR (Le Borgne et al. 2004).

The general idea is to approximate each wavelength bin with a polynomial function of , log g and [Fe/H]. This process resembles to the fitting functions (Worthey et al. 1994) that are used to predict the equivalent width of some features, or spectrophotometric indices given some atmospheric parameters. It is extended to model every spectral point.

This is a global interpolation, in the sense that each polynomial is valid in a wide range of parameters. An alternative would be to use a local interpolation, like averaging the nearest spectra to a given point in the parameters’ space. A good example of local interpolation is Vazdekis et al. (2003). Both methods have their own advantages and inconvenients. The global interpolation is less sensitive to the stochasticity of the distribution of the stars, but may not respond accurately in the regions where the spectrum changes rapidly. It is also continuous and derivable everywhere, which are required properties to use it as a function for non-linear fit, as in ULySS. In both cases it is possible to control the quality of the interpolation by comparing each star to the interpolated spectrum which match its parameters.

For the present work, we use the same polynomial developments as for ELODIE 3.2, because this will permit to use it directly as a model for a TGM component in ULySS. The first version of this interpolator was described in Prugniel & Soubiran (2001), and we remind below the principles and present the difference introduced in ELODIE 3.2.

regimes

The library contains all types of stars, from O to M, and the temperature is the main parameter controlling the shape of the spectra. Modelling all the stars with a single set of polynomials would necessitate to include a large number of terms. The result would accordingly be very unstable, presenting oscillations and violently diverging near the edges of the parameters’ space. For this reason, we defined three temperature ranges, matching the OBA, FGK and M spectral types, where independent set of polynomials are adjusted. These three regimes have comfortable overlaps, allowing us to connect them smoothly by a linear interpolation. The limits are:

OBA regime:

 K

FGK regime:

 K

M regime:

 K

Note that the M regime encompasses the cool K-type stars.

Polynomial developments

The developments are the same as for ELODIE.3.2, but truncated to exclude the rotation terms introduced to suppress a bias due to a degeneracy between the stellar rotation and the temperature (see Wu et al. 2011). Because of the lower spectral resolution of MILES, the stellar rotation is mixed with the variation of the resolution from star to star, and the introduction of these terms did not appear relevant.

The terms were chosen iteratively, adding at each step the one leading to the largest reduction of the residuals between the observations and the interpolated spectra. The following developments were used:

(3)

TGM is a flux-calibrated interpolated spectrum. Unlike for ELODIE, we did not compute a continuum-normalized interpolator, as it is not needed here.

The 23 terms were used for both the FGK and M regimes, but the development was truncated to the first 19 for the OBA one.

Support for extrapolation

One of the limitation of using empirical libraries is that they do not cover all the range of atmospheric parameters we may wish they would. In particular, to study the stellar populations of galaxies, we would need, for example, young stars of low metallicity, which are obviously missing in a library of Galactic stars.

For this reason, it is important that the interpolator preserves its quality at the edges of the parameters’ space, where only rare stars are present. This is a difficulty for any type of interpolating function.

A solution could have been to supplement the library with theoretical spectra in the margins of the parameters’ space. However, this would introduce discontinuities because the flux scale of theoretical spectra is not fully consistent with the empirical library. To improve this situation, Prugniel et al. (2007a) introduced a semi-empirical solution where theoretical spectra are used differentially to extend the coverage of the parameters’ space. This was used to add a [Mg/Fe] dimension to the space, and to model spectra with non-solar abundances, as Galactic globular clusters (Prugniel et al. 2007a; Koleva et al. 2008). The same principle was adopted in ELODIE 3.2 to extend the range of the 3-dimensional parameters’ space (without the [Mg/Fe] dimension which is not taken into consideration neither in ELODIE 3.2 nor in the present paper).

We compute the differential effect of changing a parameter between a point belonging to the empirical library, and another one located outside of the range of the library. This differential spectrum is built using a theoretical library. Finally, we produce a semi-empirical spectrum, summing the differential one to one generated with the initial version of then interpolator (computed without the semi-empirical extrapolation supports) at the reference location.

We used the Martins et al. (2005) library to add semi-empirical spectra at the following locations: (i)  K, log  4 and 4.75, and [Fe/H] , 0 and +0.3 dex, using as reference 20000 K, log  3.5, [Fe/H] 0; (ii)  K, log  3.5 and 4.75, and [Fe/H] , and +0.3 dex, using as reference 30000 K, log  3.5, [Fe/H] 0; (iii)  K, log  3, and 5 and [Fe/H] , using as reference 20000 K, log  3.5, [Fe/H] 0. We also used the Coelho et al. (2005) library to add some low metallicity cool dwarfs at the locations 3500 K, log  4.5 and 5.0, [Fe/H]  and using as reference 3500 K, log  4.5, [Fe/H] .

We affect a low weight to these spectra, and they do not affect the region populated with observed stars: Each extrapolation-support spectrum has 1/20th of the weight of an observed star. We computed a final version of the interpolator, using the semi-empirical spectra to prevent the divergence at the edges of the parameters’ space and to extend the validity range. The interpolated spectra in the extrapolated regions are probably not very accurate, but they do not diverge and are sufficient for many applications.

3 Results

In this section, we present the results of the previous procedure. We discuss the determination of the LSF, the measurements of the atmospheric parameters, and finally the computation and validation of the interpolator.

Figure 1: Line-spread function for 10 stars of the MILES library chosen arbitrarily (actually 10 of the first 12), using for reference the interpolated ELODIE spectra. The top panel shows the residual shift of the spectra, illustrating the precision of the wavelength calibration and of the rest-frame reduction. The bottom panel presents the FWHM resolution. The mean formal error on each LSF point is of 0.5 km s on the residual shift, and 0.025 Å on the FWHM resolution
Figure 2: Histograms of the broadening and residual shift of the line-spread function of the MILES library at 5300 Å. The green histograms are for the 404 direct comparisons with spectra of the ELODIE library. The red ones are the comparisons with the ELODIE interpolator. The top panel is the distribution of the residual shifts, in km s, and the bottom ones the distribution of the FWHM Gaussian broadening.

3.1 Line-spread function and wavelength calibration

The broadening was determined individually by comparing MILES and ELODIE spectra for all the stars in common. In order to increase the statistics, we also made the analysis for all the MILES stars by comparing them with the ELODIE interpolated spectrum corresponding to their atmospheric parameters. This second set of LSFs includes both the instrumental and physical broadening of the individual stars.

Figure 1 presents the individual LSF (using the ELODIE interpolated spectra as reference) for some stars chosen arbitrarily (the firsts of the list). From this small subset alone, it is apparent that the broadening is variable. Some spectra have a lower effective resolution, possibly due to stellar rotation, and some have a higher resolution, maybe because of a better focusing of the spectrograph. It also appears that the rest-frame reduction is not always accurate, with deviation reaching a few 10 km s. This may be due to (i) uncertain knowledge of the heliocentric velocities, (ii) imperfect wavelength calibration, or (iii) stellar duplicity. We note also that the residual shift often changes with the wavelength by 10 to 30 km s over the ELODIE range. This results from an uncertainty in the dispersion relation. The effect is slightly larger that the precision estimated in Sánchez-Blázquez et al. (2006). The values of the broadening and residual shift at 5300 Å are given for each star in Table 4.

The histograms of the broadening and residual shifts are presented on Fig. 2. The gaussian broadening at 5300 Å spans the range km s, (i. e. Å) and the histogram is skewed toward the large dispersions. This is likely due to the effect of the rotation. The mean broadening at the same wavelength is 60.5 km s, for the direct comparison, with the rms dispersion of 2.4 km s(i. e. respectively 2.52 and 0.10 Å for the FWHM). The mean broadening is similar (60.9 km s) and the spread slightly larger (3.6 km s), when interpolated spectra are used. The consistency between the two determinations shows that the physical broadening is only a minor contribution.

As expected, the residual shifts essentially cancel in the mean LSF. The mean shift is 2 km s(identical for the two analysis), in the sense that MILES is red-shifted. The internal rms spread of these residual shifts is 12 km s or FWHM=0.50 Å at 5300 Å. If MILES is used to compute population models, this will be combined with the instrumental broadening. In other words, the resolution of an interpolated MILES spectrum, or of a population model, will be: Å. (assuming that the effect is uniform over all the parameters’ space).

The mean absolute difference of the residual velocity between the last and first segment of the LSF (i. e. between 6500 and 4100Å) is 15 km s. This reflects the accuracy of the dispersion relation used for the wavelength calibration. As explain in Sánchez-Blázquez et al. (2006), to save observing time, arc spectra were not acquired for each individual spectrum, but only for some spectra representative of each spectral type and luminosity class. It was assumed that the linear dispersion and higher order terms of the dispersion relation were constant, and a global shift was determined by cross-correlating each spectrum to a well-calibrated one. Our present test indicates that the stability of the spectrograph was slightly over-estimated, and the variation of the linear term of the dispersion relation will contribute for a further degradation of the LSF for population models.

Figure 3: Mean line-spread function of the MILES library, using for reference (i) the interpolated ELODIE spectra (red line and symbols) and (ii) the ELODIE spectra of stars in common (green). The top panel gives the mean residual shift over all the library, and the bottom panel the mean FWHM wavelength resolution. The bars indicate the errors on the mean value (i. e. dispersion / ). The abscissa of the red symbols are shifted by a small quantity to avoid superposition.

The variation of the LSF with wavelength, presented in Fig. 3, is consistent for the two sets of templates. The resolution changes from 2.45 Å at 4000 Å, to 2.63 Å at 6500 Å, with an average value of 2.56 Å. This is broader than the estimation in Sánchez-Blázquez et al. (2006). In this paper, the authors found FWHM=2.3 Å by comparison with CFLIB, for which they assumed a resolution of 1 Å. In fact, the resolution of CFLIB is rather 1.4 Å (Wu et al. 2011; Beifiori et al. 2010), and correcting this error put the two values in agreement. Beifiori et al. (2010) also measured the resolution of MILES with a similar method and found 2.55 Å, independent of the wavelength. This is consistent with our result.

It is also interesting to characterize the LSF over the whole MILES wavelength range. We therefore repeated the analysis using the Coelho et al. (2005) library. We found consistent results in the ELODIE range, but with a larger spread, certainly due to lower quality fits. The residuals are typically three times larger than those obtained when we compared to ELODIE. A consequence is that the trend of the LSF with the wavelength is smeared out, leaving a uniform  Å. We similarly analysed the MILES spectra of the five closest analogs of the Sun against the high resolution spectrum from Kurucz et al. (1984). The results are also consistent with those obtained with ELODIE, but with a large spread resulting from the small statistics. Therefore we cannot constrain the resolution outside of the wavelength range of ELODIE with the same accuracy.

Figure 4: Distribution in the log() - log  and log() - [Fe/H] planes of the adopted atmospheric parameters for the 985 MILES stars. In the top panel, the color of the symbols distinguishes different metallicity classes. In the bottom panel, it distinguishes different classes of surface gravity.
Figure 5: Comparison of the measured atmospheric parameters with the Cenarro et al. (2007) compilation. The abscissae are the parameters measured in the present paper.

3.2 Atmospheric parameters

We measured the atmospheric parameters for the 985 spectra as indicated in Sect. 2.4. As it is known from Wu et al. (2011), the automatic determination is highly reliable for the FGK stars, but lack of faithfulness in some regions of the parameters’ space. Namely, this concerns the hot evolved stars and the cool stars ( K). Therefore, for the stars found in these regimes, we searched the literature for recent determinations based on high-resolution spectroscopy. We also examined the very low-metallicity stars, and those for which our derived parameters depart significantly from those listed in Cenarro et al. (2007). Whenever we found values judged more credible than ours, we adopted them.

For 77 stars (8% of the library), we adopted parameters compiled and averaged from the literature. For four of them HD 18191, 17491, 54810 and 113285, we adopted either the metallicity or the gravity from the internal inversion of the MILES interpolator (see. Sect. 3.3). For six stars (one A-type star with emission line HD 199478, and five cool stars,  K, G 156-031 and 171-010, HD 113285, 126327 and 207076) we could not find any reliable source for at least one of the atmospheric parameters.

The most metal poor star of the library, HD 237846, belongs to a stream discovered by Helmi et al. (1999). We adopted [Fe/H] from recent measurements (Zhang et al. 2009; Ishigaki et al. 2010; Roederer et al. 2010), while Cenarro et al. (2007) catalogued [Fe/H]. The inversion with ELODIE returned [Fe/H]. The fitted metallicity values for the low metallicity stars ([Fe/H]) were often biased toward higher values by dex. For 13 of these 46 metal deficient stars, we adopted parameters from the recent literature

The adopted parameters are listed in Table 4, also available in Vizier. Figure 4 shows the distribution of the stars in the vs. log  and vs. [Fe/H] diagrams.

Comparison N8 log g (cm s) [Fe/H] (dex)
Cenarro OBA 121 2.1 % 7.9 % 0.080 0.384 0.101 0.408
FGK 773 46 K 120 K 0.038 0.284 0.045 0.133
M 91 -49 K 165 K 0.039 0.317 0.012 0.283
ELODIE OBA 48 -3.2 % 4.7 % 0.026 0.218 0.009 0.069
FGK 332 12 K 60 K 0.008 0.079 0.038 0.055
M 23 -3 K 16 K -0.022 0.200 0.034 0.061
CFLIB OBA 42 -2.2 % 6.0 % -0.016 0.268 0.025 0.110
FGK 309 2 K 43 K -0.025 0.069 0.021 0.030
M 16 16 K 9 K 0.051 0.173 0.028 0.061
9
Table 2: Comparison of the atmospheric parameters with other studies.

We compared, in Fig. 5, our parameters to those from Cenarro et al. (2007). We also compared our results with ELODIE 3.2 and CFLIB (Wu et al. 2011), for the stars in common, and the corresponding statistics are shown in Table 2.

The mean deviations with Cenarro et al. are larger than those obtained by Wu et al. (2011) for the CFLIB library. For example, the dispersion is 120 K for the FGK stars, while Wu et al. report dispersions of  70 K when comparing to homogeneous measurements based on high-resolution spectroscopy, and  100 K when comparing to the compilation of Valdes et al. (2004). For the two other parameters, the dispersion is consistent with the comparison between CFLIB and the Valdes et al. (2004) compilation. The comparisons with the ELODIE 3.2 and CFLIB parameters obtained with the same method, are typical of comparisons between accurate spectroscopic measurements.

There is a statistically significant bias on of the FGK stars (47 K) between our measurements and Cenarro et al. (2007). Although this is within the uncertainties of the present calibrations, such a bias has consequences when the library is used in models of stellar populations. As pointed in some occasions (Prugniel et al. 2007a; Percival & Salaris 2009), it is sufficient to alter the age derived for old globular clusters by several Gyr.

We compared our measurements with González Hernández & Bonifacio (2009) who used the infrared flux method to measure for FGK stars using 2MASS photometry. After clipping 9 outliers out of the 232 stars in common, we found that these values are in average 28 K warmer than ours, with a dispersion of 141 K. Vazdekis et al. (2010) compared the Cenarro et al. (2007) and González Hernández & Bonifacio (2009) measurements and found a bias of 59 K of the same sign. Our measurements are in better agreement with González Hernández & Bonifacio (2009) than the original MILES compilation, but the different values of the bias are within the accuracy of the determination of the temperature scale and are only marginally significant.

We used the statistics of the comparison with Cenarro et al. to estimate the external error. We used the ratios of the differences between the two series to the formal errors to rescale the errors, conservatively assuming that the mean precisions of each series are equivalent. This rescaling factor depends on the temperature. It changes from 5 for the G stars to about 20 for both the hottest and the coolest stars. These factors are the same for the three parameters, and the same order of magnitude than those used in Wu et al. (2011). The external errors are significantly larger than the formal error for several reasons, including the internal degeneracies between the atmospheric parameters. They are reported in Table 4.

For the FGK stars the mean errors are 60 K, 0.13 and 0.05 dex respectively for , log g and [Fe/H]. For the M stars, they are 38 K, 0.26 and 0.12 dex, and for the OBA 3.5%, 0.17 and 0.13 dex. The figures are similar to the precision reported by Wu et al. (2011), implying that there is no degradation of the performance of the method because of the lower spectral resolution.

3.3 Interpolator

We adjusted an interpolator to all the stars in MILES, using the atmospheric parameters of Table 4. For the 27 stars presenting a mean residual velocity shift greater than 30 km s, we shifted the spectra by an integer number of pixels to reduce the effect. We did not correct all the spectra for the wavelength dependent shifts derived in Sect. 3.1 to avoid a rebinning by fractions of pixel. We affected a weight to each star depending on its location in the parameters’ space, in order to compensate the uneven distribution of the stars. The low-metallicity stars, and the coolest and hottest ones were over-weighted because they are in relative small numbers. We did not weight with the signal-to-noise of the spectra because this information is not available.

We checked the residuals between the observed and interpolated spectra to identify and correct outliers. Finally, we assessed the validity of this interpolator performing two tests: (i) We compared the original and interpolated spectra, and (ii) we used the interpolator to measure the atmospheric parameters of MILES and CFLIB with ULySS.

Detection and treatments of the outliers

We started with all the stars, and we examined the residuals between the observed and interpolated spectra. There are a priori different causes for these residuals: (i) Although “normal” stars were targeted, some peculiarities affect some of them (binarity, rotation, chromospheric emission, non-typical abundances …), (ii) the atmospheric parameters derived in Sect. 3.2 have uncertainties (or errors), and (iii) the MILES spectra have uncertainties.

The most prominent outliers correspond to spectra whose shape disagree with the interpolator. This is probably not because of errors in the atmospheric parameters, as the spectral features are generally well fitted, but rather because of errors in the flux calibration or in the correction for Galactic extinction. We nevertheless searched the literature for indications of peculiarities that may explain the discrepancies, and whenever we found some plausible reason we excluded the star from the computation of the interpolator. We observe that the spectra with wrong continuum shape are often located at low Galactic latitude or in obscured regions. The most deviant example is HD 18391, a Cepheid variable whose extinction was corrected assuming E(B-V) 0.205 mag. Our spectroscopic fit indicates a considerably higher extinction, consistently with Turner et al. (2009) who derived E(B-V) 1 mag. Another example where the extinction was under-corrected by  0.7 mag is HD 219978. Although the main outliers corresponds to underestimated extinctions, there are cases of over-estimation, like HD 76813.

We suppose that the main source of discrepancy is the correction of the Galactic extinction, but we cannot safely separate between this possibility and an error on the flux calibration. Nevertheless, we assumed that for those discrepant cases, the error is due to the extinction correction and we applied an additional correction using a Galactic extinction curve (Schild 1977). Whenever this correction was unsatisfactory (maybe because the source of error is the flux-calibration), we flagged the spectrum to have a reduced weight or to be excluded. We corrected the extinction for 55 field stars.

All this process was made iteratively, treating the most prominent outliers and recomputing a further version of the interpolator. Finally, the mean residuals between the interpolated and observed spectra is 4%, a value comparable to what is obtained for the ELODIE interpolator. A large fraction of these residuals are still due to mismatches of the shape of the continuum.

Figure 6: Fits of MILES spectra with the MILES interpolator for three representative stars. For each star, the top panel represents the flux distribution, normalized to an average of one, and the bottom ones the residuals between the observation and the best fitted interpolated spectrum (observationmodel). The fit was performed with ULySS. The continuous green lines are the errors, assuming a constant error spectrum and . The clear blue lines are the multiplicative polynomials.
Comparison N10 log g (cm s) [Fe/H] (dex)
MILES11 OBA 130 1.2 % 4.5 % -0.002 0.202 0.016 0.118
FGK 770 -3.0 K 68.7 K -0.024 0.108 0.005 0.074
K5-M 85 -7.7 K 31.5 K 0.011 0.177 0.039 0.082
M 26 2.1 K 35.4 K 0.092 0.219 0.045 0.102
BHB 25 2.7 % 9.1 % -0.095 0.541 -0.092 0.331
CFLIB12 OBA 231 -1.7% 6.3% -0.027 0.214 0.012 0.157
FGK 960 -20.9 K 75.4 K -0.072 0.104 0.014 0.063
K5-M 74 6.4 K 33.6 K 0.069 0.219 0.089 0.086
M 24 31.8 K 34.1 K 0.16313 0.142 0.17914 0.119
BHB 28 -6.9% 11.0% -0.243 0.623 -0.707 0.688
15
Table 3: Tests of the interpolator.

Tests of the interpolator

Wu et al. (2011) have shown that the ELODIE interpolator is not reliable for the hot evolved stars nor for the very cool stars. It is not known if the reason resides in the limited sampling of the parameters’ space in these regions or from more fundamental characteristics of the interpolator. In order to check this, we used the new interpolator to measure the atmospheric parameters of MILES and CFLIB. The first one is an internal test, where each MILES spectrum is compared to the interpolator based on the whole library. The statistics of the comparisons between these new sets of parameters and the adopted ones are summarized in Table 3.

For the coolest stars (), the metallicities measured with the ELODIE interpolator were biased toward low values. This effect is absent with the MILES interpolator. For the hot evolved stars ( and [Fe/H]  dex) the biases are also considerably reduced compared to those obtained with the ELODIE interpolator.

Figure 6 presents the fits with the MILES interpolator of the MILES spectra of three stars of different spectral types. The residuals are of the order of 1% of the flux, and the multiplicative polynomials are flat and close to unity, reflecting the good quality of the flux calibration of MILES.

These tests show that the MILES interpolator is reliable to measure the atmospheric parameters over their whole range. The lower resolution of MILES do not affect these determinations. The FITS file containing the coefficients of the interpolator is available in Vizier. It can be directly used in ULySS to fit stellar spectra. The interpolated spectra can also be computed online in a Virtual Observatory compliant format (Prugniel et al. 2008).

3.4 Discussion on the flux-calibration

The presumably good flux-calibration of MILES is its most attractive characteristics. It was assessed by comparison with accurate broad-band photometry. In order to test the photometric precision of the interpolator, we fitted the residuals between the observed and interpolated spectra with a straight line and expressed the result as a colour. For the whole library, we find  mag and  mag, respectively for the bias and dispersion. This residual colour is by construction small, since the interpolator was built with the observed spectra, but the small dispersion reflects the good spectrophotometric precision. The photometric precision on the individual spectra were determined by Sánchez-Blázquez et al. (2006) to be  mag, by comparing synthetic colours with different sets of standards (the two numbers corresponds to different standards). Our present values are not as precise, likely because they also include the errors on the Galactic extinction corrections, on the atmospheric parameters and the cosmic variance introduced by characteristics of individual stars that are not considered in the interpolator.

We can also assess the photometric accuracy of the ELODIE library. This has always been a question, because its flux-calibration results from a complex and indirect process. To test it, we made series of interpolated ELODIE spectra following the main and giant sequences and we computed the photometric precision as above. We found that the differences between the interpolated ELODIE and MILES spectra are  mag, which is consistent with the estimations made in (Prugniel & Soubiran 2004; Prugniel et al. 2007b).

4 Summary and conclusion

We derived the atmospheric parameters of the stars of the MILES library. We estimated the external precision for the FGK stars to be 60 K, 0.13 and 0.05 dex respectively for , log g and [Fe/H]. For the M stars, the mean errors are 38 K, 0.26 and 0.12 dex, and for the OBA 3.5%, 0.17 and 0.13 dex. This precisions are comparable to those obtained with the same method for the CFLIB library, whose resolution is significantly higher. This shows that there is no significant degradation due to the resolution.

We characterized the LSF. We found that the residual shift of the rest-frame reduction has a dispersion of 12 km s, with an average of 2 km s (MILES is slightly red-shifted). The mean FWHM dispersion of the library is 2.56 Å, changing from 2.45 to 2.63 Å from the blue to the red.

We computed an interpolator for the library. This is a function returning a spectrum for given , log g and [Fe/H]. In order to check its reliability, we used it to derive the atmospheric parameters of MILES itself and CFLIB. The results are in good agreement with those derived with the ELODIE interpolator in the present paper and in Wu et al. (2011). For some regimes where the ELODIE interpolator has shown deficiencies (hot evolve stars and cool stars), the MILES interpolator has better performances.

In a companion paper, we will use this interpolator to prepare stellar population models using PEGASE.HR.

Acknowledgements.
We thank the referee for her/his constructive comments. We acknowledge the support from the French Programme National Cosmologie et Galaxies (PNCG, CNRS). MK has been supported by the Programa Nacional de Astronomía y Astrofísica of the Spanish Ministry of Science and Innovation under grant AYA2007-67752-C03-01. She thanks CRAL, Observatoire de Lyon, Université Claude Bernard, Lyon 1, for an Invited Professorship.
\onltab

1

Name Miles16 error error [Fe/H] error cz17 18 references
(K) () (dex)
HD 224930 0001 5411 36 4.19 0.07 -0.78 0.04 -5 59 0
HD 225212 0002 4179 68 0.85 0.19 0.18 0.08 21 53 0
HD 225239 0003 5559 44 3.72 0.09 -0.51 0.05 -18 59 0
HD 00004 0004 6779 66 3.87 0.07 0.21 0.04 1 65 0
HD 00249 0005 4766 53 2.91 0.13 -0.27 0.06 0 58 0
HD 00319 0006 8641 150 4.29 0.09 -0.35 0.10 -18 55 0
HD 00400 0007 6190 51 4.15 0.08 -0.22 0.04 -14 63 0
HD 00245 0008 5749 39 4.13 0.07 -0.57 0.04 -14 62 0
HD 00448 0009 4800 50 2.63 0.12 0.04 0.05 9 59 0
BD+13 0013 0010 5000 3.00 -0.75 29
HD 00886 0011 20454 819 3.79 0.14 -0.03 0.08 -15 47 0
HD 01326B 0012 3679 30 4.92 0.11 -1.15 0.19 -33 57 0
HD 01461 0013 5666 42 4.21 0.08 0.19 0.04 2 62 0
HD 01918 0014 4888 55 2.44 0.14 -0.40 0.06 -6 59 0
HD 02628 0015 7335 69 3.95 0.06 -0.09 0.05 -17 49 0
HD 02665 0016 4986 77 2.28 0.21 -1.96 0.08 -19 60 0
HD 02796 0017 4837 95 1.78 0.24 -2.23 0.10 -2 63 0
HD 02857 0018 8000 2.70 -1.50 1,4
HD 03008 0019 4289 60 0.72 0.13 -1.87 0.06 -8 57 0
HD 03369 0020 16005 596 3.71 0.28 0.04 0.17 -46 152 0
HD 03360 0021 20375 730 3.80 0.11 -0.04 0.07 -11 44 0
HD 03567 0022 6094 50 4.18 0.07 -1.14 0.06 -4 63 0
HD 03546 0023 4945 69 2.36 0.17 -0.66 0.08 9 57 0
HD 03574 0024 4048 28 1.13 0.14 0.10 0.05 1 55 0
HD 03651 0025 5211 46 4.48 0.08 0.21 0.04 10 59 0
HD 03795 0026 5345 41 3.72 0.09 -0.63 0.04 -3 58 0
HD 03883 0027 7616 125 3.81 0.11 0.68 0.06 -10 56 0
HD 04307 0028 5773 38 3.97 0.08 -0.24 0.04 -3 62 0
HD 04395 0029 5444 51 3.43 0.11 -0.27 0.05 1 53 0
HD 04539 0030 25000 5.40 0.00 39,2
HD 04628 0031 4964 59 4.65 0.10 -0.23 0.06 2 62 0
HD 04656 0032 3956 38 1.85 0.25 -0.09 0.08 -1 54 0
HD 04744 0033 4638 61 2.35 0.18 -0.64 0.07 -7 58 0
HD 04906 0034 5157 51 3.58 0.12 -0.66 0.05 -6 58 0
HD 05268 0035 4904 83 2.35 0.21 -0.57 0.10 10 57 0
HD 05384 0036 3964 34 1.95 0.21 0.16 0.06 4 59 0
HD 05395 0037 4870 61 2.43 0.15 -0.40 0.07 -1 59 0
HD 05780 0038 3943 46 1.75 0.33 -0.56 0.14 -5 57 0
HD 05916 0039 4954 59 2.31 0.15 -0.75 0.07 3 56 0
HD 06186 0040 4865 51 2.36 0.13 -0.35 0.06 8 58 0
HD 06203 0041 4565 58 2.28 0.16 -0.32 0.06 4 58 0
HD 06268 0042 4735 113 1.42 0.26 -2.36 0.11 4 58 0
HD 06229 0043 5181 57 2.50 0.15 -1.14 0.07 3 60 0
HD 06474 0044 6781 214 0.49 0.13 0.26 0.09 -16 55 0
HD 06497 0045 4448 66 2.75 0.18 0.04 0.08 8 53 0
HD 06582 0046 5323 35 4.33 0.07 -0.79 0.04 -14 61 0
HD 06805 0047 4563 82 2.61 0.21 0.12 0.08 12 62 0
HD 05848 0048 4499 48 2.27 0.13 0.14 0.05 8 60 0
HD 06834 0049 6482 52 4.22 0.06 -0.58 0.05 18 62 0
HD 06755 0050 5097 60 2.53 0.16 -1.58 0.07 -7 59 0
HD 06833 0051 4596 63 1.90 0.17 -0.70 0.08 -12 59 0
HD 07106 0052 4701 43 2.58 0.10 0.01 0.05 10 60 0
HD 07351 0053 3548 49 0.83 0.54 -0.23 0.23 0 59 0
HD 07374 0054 12247 388 4.16 0.12 0.16 0.14 6 50 0
HD 07595 0055 4349 75 1.72 0.24 -0.57 0.11 1 61 0
HD 07672 0056 4939 66 2.78 0.16 -0.42 0.07 -9 60 0
HD 08724 0057 4772 80 1.69 0.21 -1.64 0.10 -2 60 0
HD 08829 0058 7129 67 4.10 0.06 -0.17 0.05 9 54 0
HD 09138 0059 4078 42 1.94 0.21 -0.40 0.09 -9 55 0
HD 09356 0060 6800 83 4.24 0.06 -0.80 0.07 29 56 0
HD 09562 0061 5766 45 3.89 0.09 0.14 0.04 11 61 0
HD 09408 0062 4814 115 2.46 0.28 -0.31 0.13 33 57 0
HD 09826 0063 6139 36 4.06 0.06 0.11 0.03 1 60 0
HD 09919 0064 6860 4.00 -0.35 2,3,24
HD 10380 0065 4170 49 1.91 0.21 -0.16 0.08 6 59 0
HD 10307 0066 5875 40 4.28 0.07 0.06 0.03 5 60 0
HD 10700 0067 5348 45 4.39 0.09 -0.46 0.05 3 58 0
BD+72 0094 0068 6131 49 4.09 0.06 -1.68 0.07 11 70 0
HD 10780 0069 5406 44 4.63 0.08 0.15 0.04 -9 59 0
HD 10975 0070 4872 63 2.46 0.15 -0.22 0.07 4 57 0
HD 11257 0071 7103 75 4.08 0.06 -0.27 0.06 -4 53 0
HD 11397 0072 5526 53 4.24 0.10 -0.58 0.05 5 57 0
HD 11964 0073 5272 55 3.85 0.11 0.05 0.05 7 59 0
HD 12014 0074 4371 108 0.66 0.16 0.04 0.10 20 58 0
HD 12438 0075 4937 86 2.35 0.21 -0.73 0.10 1 61 0
HD 13043 0076 5823 43 4.11 0.08 0.06 0.03 -11 62 0
BD+29 0366 0077 5666 31 4.25 0.06 -0.95 0.04 4 59 0
HD 13267 0078 15500 2.57 -0.10 3,10
HD 13555 0079 6515 54 4.07 0.07 -0.16 0.04 -11 60 0
HD 13520 0080 4043 30 1.66 0.17 -0.16 0.06 -5 57 0
BD-01 0306 0081 5723 43 4.28 0.08 -0.89 0.05 25 62 0
HD 13783 0082 5516 42 4.37 0.08 -0.49 0.04 -3 60 0
HD 14221 0083 6619 52 4.07 0.06 -0.17 0.04 13 60 0
HD 14802 0084 5777 70 3.89 0.14 -0.07 0.06 10 61 0
HD 14829 0085 8750 3.15 -1.57 2,27
HD 14938 0086 6275 62 4.22 0.09 -0.25 0.05 -3 64 0
HD 15596 0087 4859 80 2.77 0.21 -0.63 0.09 2 60 0
HD 15798 0088 6527 59 4.07 0.07 -0.12 0.04 -2 59 0
HD 16031 0089 6039 53 4.09 0.07 -1.63 0.07 -16 70 0
HD 16234 0090 6225 42 4.18 0.06 -0.19 0.04 14 63 0
HD 16232 0091 6314 55 4.29 0.07 0.11 0.04 0 62 0
HD 16673 0092 6260 44 4.30 0.06 0.00 0.03 -19 63 0
HD 16784 0093 5782 48 4.08 0.09 -0.68 0.05 0 59 0
BD+46 0610 0094 5889 44 4.13 0.08 -0.86 0.05 -1 59 0
G 004-036 0095 6073 55 4.20 0.08 -1.66 0.08 2 70 0
HD 16901 0096 5729 45 0.95 0.07 0.03 0.04 -5 57 0
HD 17081 0097 12722 490 4.20 0.13 0.28 0.16 7 58 0
HD 17361 0098 4662 52 2.61 0.13 0.06 0.05 16 59 0
HD 17491 0099 3200 0.60 -0.08 2,0b
HD 17382 0100 5339 37 4.64 0.06 0.17 0.04 -6 59 0
HD 17548 0101 6013 49 4.20 0.08 -0.53 0.05 3 59 0
HD 17378 0102 8477 96 1.25 0.06 0.00 0.09 3 58 0
HD 18191 0103 3250 0.30 -0.24 2,0b
HD 18391 0104 5750 1.20 -0.13 2,20,35,36
HD 18907 0105 5069 47 3.43 0.11 -0.65 0.05 -9 61 0
HD 19445 0106 5900 4.20 -2.07 0,46
HD 19510 0107 6108 3.91 -2.13 1
HD 19373 0108 5947 47 4.15 0.08 0.11 0.04 16 58 0
HD 19994 0109 6051 41 4.02 0.07 0.16 0.03 10 60 0
HD 20041 0110 11509 385 2.01 0.10 0.23 0.16 0 65 0
HD 20512 0111 5267 44 3.81 0.09 -0.13 0.04 3 60 0
HD 20619 0112 5710 41 4.47 0.07 -0.18 0.04 2 59 0
HD 20630 0113 5733 36 4.45 0.06 0.12 0.03 -3 58 0
HD 20893 0114 4383 45 2.29 0.14 0.14 0.05 0 59 0
BD+43 0699 0115 4732 55 4.68 0.10 -0.33 0.06 12 61 0
HD 21017 0116 4443 45 2.74 0.12 0.12 0.05 8 58 0
HD 21197 0117 4363 43 4.50 0.09 0.14 0.05 10 61 0
HD 21581 0118 5031 80 2.45 0.21 -1.51 0.09 21 59 0
BD+66 0268 0119 5300 4.20 -2.00 0,52,53,54
HD 22049 0120 5115 41 4.72 0.07 0.05 0.04 0 61 0
HD 22484 0121 5987 46 4.07 0.08 -0.05 0.04 10 60 0
HD 21910 0122 4822 77 2.44 0.19 -0.42 0.09 -6 61 0
HD 22879 0123 5870 46 4.23 0.08 -0.80 0.05 7 60 0
HD 23249 0124 5020 55 3.73 0.11 0.08 0.05 6 57 0
HD 23261 0125 5165 53 4.56 0.09 0.24 0.05 -21 60 0
HD 23194 0126 8031 105 4.00 0.08 -0.17 0.07 8 52 0
HD 23439A 0127 5181 38 4.47 0.08 -0.90 0.05 -15 58 0
HD 23439B 0128 4838 49 4.61 0.09 -0.91 0.07 -1 59 0
HD 23607 0129 7586 94 3.97 0.07 -0.03 0.06 1 54 0
HD 23841 0130 4341 60 2.10 0.20 -0.53 0.09 -2 58 0
HD 23924 0131 7776 116 3.94 0.09 0.07 0.07 5 56 0
HD 24616 0132 5014 56 3.16 0.14 -0.71 0.06 24 61 0
HD 24341 0133 5405 43 3.71 0.09 -0.62 0.05 0 57 0
HD 24421 0134 6168 47 4.20 0.07 -0.29 0.04 4 62 0
HD 24451 0135 4427 42 4.63 0.08 -0.09 0.05 -1 62 0
HD 25329 0136 4982 47 4.65 0.09 -1.48 0.08 1 61 0
HD 25532 0137 5600 2.50 -1.35 0,4
HD 25673 0138 5112 49 4.54 0.09 -0.40 0.06 12 61 0
HD 26297 0139 4479 72 1.05 0.18 -1.78 0.09 -5 60 0
HD 281679 0140 8542 2.50 -1.43 2
HD 26322 0141 7008 53 3.94 0.05 0.13 0.04 12 57 0
BD+06 0648 0142 4522 82 1.09 0.20 -2.03 0.10 -1 61 0
HD 284248 0143 6113 47 4.14 0.06 -1.55 0.06 5 73 0
BD-06 0855 0144 5442 46 4.60 0.08 -0.69 0.06 1 60 0
HD 26965 0145 5114 46 4.41 0.08 -0.26 0.05 -12 61 0
HD 285690 0146 4907 55 4.63 0.09 0.21 0.05 13 62 0
HD 27126 0147 5425 51 4.14 0.10 -0.38 0.05 -13 62 0
HD 27295 0148 11034 364 3.99 0.13 -0.11 0.14 2 47 0
HD 27371 0149 4995 45 2.76 0.11 0.15 0.05 13 58 0
HD 27771 0150 5285 44 4.59 0.08 0.27 0.04 -9 58 0
HD 27819 0151 7871 98 3.89 0.08 -0.06 0.06 11 51 0
HD 28305 0152 4964 63 2.72 0.15 0.20 0.06 11 56 0
HD 285773 0153 5348 41 4.56 0.07 0.25 0.04 -7 58 0
HD 28946 0154 5314 44 4.55 0.08 -0.10 0.04 1 61 0
HD 28978 0155 8864 183 3.42 0.20 -0.26 0.11 -5 44 0
HD 29065 0156 4062 48 1.76 0.26 -0.22 0.09 -2 59 0
HD 29139 0157 3870 21 1.66 0.16 -0.04 0.05 -11 59 0
BD+50 1021 0158 5081 48 4.48 0.09 -0.65 0.06 -7 63 0
BD+45 0983 0159 5155 65 4.45 0.12 -0.22 0.07 -12 61 0
HD 30743 0160 6484 56 4.16 0.07 -0.34 0.05 13 60 0
HD 30504 0161 4056 31 1.79 0.17 -0.33 0.07 3 57 0
HD 30649 0162 5791 40 4.21 0.07 -0.48 0.04 9 59 0
HD 31128 0163 5949 51 4.18 0.07 -1.45 0.06 30 62 0
HD 30959 0164 3465 27 0.76 0.29 -0.03 0.12 1 57 0
HD 30834 0165 4219 44 1.59 0.16 -0.24 0.06 5 58 0
HD 31295 0166 8822 4.11 -0.73 16,34
HD 31767 0167 4370 53 1.49 0.14 0.01 0.06 9 60 0
HD 32147 0168 4602 61 4.53 0.10 0.18 0.06 2 62 0
HD 32655 0169 7114 79 3.47 0.10 0.23 0.05 6 53 0
HD 33256 0170 6477 54 4.15 0.06 -0.27 0.04 19 61 0
HD 33276 0171 7223 71 3.80 0.07 0.22 0.05 -4 61 0
HD 293857 0172 5628 57 4.38 0.09 0.10 0.05 -6 58 0
HD 33608 0173 6461 64 4.03 0.09 0.21 0.04 3 61 0
HD 34538 0174 4870 54 2.96 0.13 -0.36 0.06 -10 60 0
MS 0515.4-0710 0175 5241 54 4.45 0.09 0.12 0.05 -12 57 0
HD 34411 0176 5842 43 4.16 0.08 0.08 0.03 7 57 0
HD 35155 0177 3575 69 0.77 0.72 -0.34 0.35 4 61 0
HD 35179 0178 4926 72 2.28 0.18 -0.62 0.09 -13 62 0
HD 35369 0179 4915 71 2.49 0.17 -0.24 0.08 6 60 0
HD 35296 0180 6171 63 4.31 0.09 0.01 0.05 -6 61 0
HD 35620 0181 4198 41 1.92 0.17 0.15 0.06 -5 59 0
HD 36003 0182 4345 42 4.59 0.08 -0.15 0.05 -4 61 0
HD 36395 0183 3670 4.70 0.00 0,37,23,38
HD 37160 0184 4810 54 2.74 0.14 -0.57 0.06 5 57 0
HD 37792 0185 6509 52 4.17 0.06 -0.54 0.05 9 62 0
HD 37536 0186 3780 40 0.52 0.19 0.13 0.09 -3 54 0
HD 37828 0187 4523 70 1.33 0.19 -1.36 0.09 7 57 0
HD 37394 0188 5279 39 4.60 0.07 0.20 0.04 -13 56 0
HD 37984 0189 4484 57 2.21 0.16 -0.41 0.07 -9 58 0
HD 38392 0190 4869 61 4.66 0.10 0.01 0.06 7 62 0
HD 38393 0191 6316 42 4.23 0.06 -0.09 0.03 4 59 0
HD 38007 0192 5705 31 3.98 0.06 -0.31 0.03 6 60 0
HD 38545 0193 8673 171 3.68 0.20 -0.48 0.13 9 104 0
HD 38751 0194 4853 47 2.74 0.11 0.18 0.05 0 60 0
HD 38656 0195 4943 60 2.55 0.14 -0.15 0.07 -2 60 0
HD 39364 0196 4706 56 2.44 0.16 -0.65 0.06 -3 59 0
HD 39853 0197 3883 27 1.60 0.23 -0.41 0.09 11 60 0
HD 39833 0198 5869 40 4.39 0.07 0.18 0.03 0 58 0
HD 39801 0199 3633 42 0.40 0.26 0.01 0.13 2 60 0
HD 39970 0200 12006 437 2.13 0.11 0.19 0.17 -7 76 0
HD 40657 0201 4300 53 1.83 0.18 -0.57 0.08 -3 59 0
HD 250792 0202 5554 42 4.33 0.07 -1.01 0.05 3 60 0
HD 41312 0203 4085 42 1.82 0.21 -0.60 0.09 -6 57 0
HD 41117 0204 20000 2.40 -0.12 0,0b,2,10,21
HD 251611 0205 5382 53 3.40 0.13 -1.44 0.06 -8 62 0
HD 41692 0206 14800 3.30 -0.01 1,7,25
HD 41636 0207 4711 55 2.48 0.14 -0.26 0.06 0 59 0
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