1. Determination of stellar parameters: the GAUDI archive Enrique Solano 1 Carlos Allende-Prieto 2 1.- INTA-LAEFF, Spanish-VO 2.- University of Texas, Austin, Spanish-VO 2.- University of Texas, Austin, Spanish-VO EuroVO-DCA Workshop, ESAC; Mar 2007

2. Determination of stellar parameters  Physical parametrization of stars is essential for the understanding of astrophysical phenomena (e.g. the HR diagram and stellar evolution).

3.  Advances in technology (robotic telescopes, multiobject spectrographs, automated reduction pipelines) have led to the existence of a large volume of spectroscopic data covering wide wavelength ranges. FLAMES/GIRAFFE: Up to 130 spectra. 2dF: Up to 400 simultaneous spectra. FoV: 2 deg. Redshift survey of 250000 galaxies. RAVE: 300 fibers. FoV: 6 deg. Spectroscopy of up to a million of stars. SDSS: Up to 640 fibers (DR5 > 1 000 000 spectra). … and GAIA will collect several millions of stellar spectra. Determination of stellar parameters (cnt’d) EuroVO-DCA Workshop, ESAC; Mar 2007

4.  The classical methodology, where a high degree of human intervention is present, is quite inappropriate.  Automated methods constitute the only approach to guarantee repeatability and high efficiency in the analysis of these vast datasets. Determination of stellar parameters (cnt’d) EuroVO-DCA Workshop, ESAC; Mar 2007

5. The data  COROT (COnvection, ROtation and planetary Transits, launched on December 2006) has a twofold objective: Study the stellar interiors (Asteroseismology) Discover extrasolar planets (transits)  The intrinsic nature of the Seismology Programme (long observations of a few objects) makes the target selection a critical issue. A preparatory ground-based observing programme (spectroscopy and photometry) of > 1500 potential candidates initiated well before the launch (1998). All this information is accessible from GAUDI, a VO-compliant archive hosted at LAEFF (http://sdc.laeff.inta.es/gaudi/). Our sample: GAUDI spectra observed with ELODIE also present in the ELODIE.3 library (Prugniel & Soubiran 2001 A&A 369, 1048): 801 objects / 1437 spectra.

6. EuroVO-DCA Workshop, ESAC; Mar 2007 Preprocessing  Resampling and weighted average  Radial velocity correction

7. Preprocessing (cnt’d)  Wavelength range used in the analysis: 4819.94 – 4899.81 Å (full range: 3900 – 6800 Å). Red enough to avoid the difficulties of dealing with metal opacities in the UV. Blue enough to include a statistically significant number of metallic lines for a reliable determination of the metal abundance even in metal-poor stars. Forcing the excitation and ionization equilibrium balance is insufficient to determine the triplet (Teff, logg, [M/H]). Hβ used as temperature indicator.

8. The synthetic data  Model atmospheres: Kurucz ODFNEW: No convective overshooting, better opacities and abundances.  Grid of models: 4500 < Teff < 7500 (∆:500K), 1.0 < logg < 5.0 (∆:0.5dex),, -2.5 < [Fe/H] < 0.5 (∆:0.5dex)  819 nodes The range was selected to avoid extreme conditions where Kurucz models may fail: Cool temperatures at which the contribution of molecules is not fully taken into account Departures from LTE Vanishment of spectral lines (very metal-poor object).  Resolution: R=7700. Low enough to make rotational and macro-turbulent broadening in late-type stars negligible. High enough to be able to recover information on the line profiles. EuroVO-DCA Workshop, ESAC; Mar 2007

9. The fitting procedure  The goal: Find the synthetic spectrum that best fits the observed one.  Different methodologies: Neural networks / Bayesian methods: the learning process is a time-consuming task but, one trained, the speed is very high. Minimum distance methods Do not have to be trained. Inefficient for large surveys and/or multi-parameter space.  Our choice: MDM EuroVO-DCA Workshop, ESAC; Mar 2007

10. Minimum distance method  Only three parameters (Teff, logg, [M/H])  Synthetic grid with a modest number of nodes (819)  The number of points per spectra is not high: 198. EuroVO-DCA Workshop, ESAC; Mar 2007  p=2; ωi = 1 / σi²  χ² minimization

11. Optimization: The Nelder-Mead (downhill) method  Simplicity: requires only function evaluations (not derivatives). simplex  The minimum is enclosed in a simplex (a triangle for two parameters) that is continuously diminished. EuroVO-DCA Workshop, ESAC; Mar 2007 Interpolation  Continuity and smoothness between neighboring synthetic data (a must for interpolation) is guaranteed.  Generation of interpolated synthetic models is speeded up by interpolating opacities in the plane (log ρ, log T) at any given metallicity (Koersterke, priv. comm.)

12. The results A-type F-G type K-type

13. Connecting with the ELODIE.3 EuroVO-DCA Workshop, ESAC; Mar 2007  Modelling Hβ is affected by a number of issues. Core is affected by non-LTE effects (F < 0.4 not taking into account in the fitting process). The line is formed in deep atmospheric layers where convection is significant. Normalization is difficult.  Systematic effect in Teff and, therefore, in logg and [M/H].  Results have been anchored to ELODIE.3 Careful compilation of physical parameters from the literature Comparison restricted to the most trusted ones (based on the quality flags).

14. Comparison with ELODIE.3

15. Comparisons. [Fe/H] (Cayrel) EuroVO-DCA Workshop, ESAC; Mar 2007

16. Comparison: Teff (Ribas) EuroVO-DCA Workshop, ESAC; Mar 2007

17. Future applications  Implementation of a VO service Routines for pre-processing (concatenation, radial velocity correction, rebinning, normalization). Access to Kurucz models from SVO Theoretical Data Server using TSAP. Identifying spectral lines using SLAP (ESA-VO). Obtaining the best atomic data from VO services (e.g. VALD-VO).  Application to new datasets EuroVO-DCA Workshop, ESAC; Mar 2007

18. New datasets  GAUDI spectra (others than ELODIE): FEROS, SARG, CORALIE,...  The COROT Exoplanet Programme Preparatory observations of the COROT fields 5000 dwarfs with R < 15. Multifiber spectroscopic observations to determine the parameters. Follow-up observations to Assess the planetary nature of a detected transit. Characterize the planet. EuroVO-DCA Workshop, ESAC; Mar 2007