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

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

 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 galaxies. RAVE: 300 fibers. FoV: 6 deg. Spectroscopy of up to a million of stars. SDSS: Up to 640 fibers (DR5 > spectra). … and GAIA will collect several millions of stellar spectra. Determination of stellar parameters (cnt’d) EuroVO-DCA Workshop, ESAC; Mar 2007

 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

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 ( 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.

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

Preprocessing (cnt’d)  Wavelength range used in the analysis: – 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.

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

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

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

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.)

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

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).

Comparison with ELODIE.3

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

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

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

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