Gliederung

1. Gliederung • Populäre Einführung I: Astrometrie • Populäre Einführung II: Hipparcos und Gaia • Wissenschaft aus Hipparcos-Daten I • Wissenschaft aus Hipparcos-Daten II • Hipparcos: Technik und Mission • Astrometrische Grundlagen • Hipparcos Datenreduktion Hauptinstrument • Hipparcos Datenreduktion Tycho • Gaia: Technik und Mission • Gaia Global Iterative Solution • Wissenschaft aus Gaia-Daten • Sternklassifikation mit Gaia • SIM und andere Missionen

2. Sternklassifikation mit Gaia

3. Object classification/physical parametrization • classification as star, galaxy, quasar, supernovae, solar system objects etc. • determination of physical parameters: • - Teff, logg, [Fe/H], [/H], A(), Vrot, Vrad, activity etc. • combination with parallax to determine stellar: • - luminosity, radius, (mass, age) • use all available data (photometric, spectroscopic, astrometric) • must be able to cope with: • - unresolved binaries (help from astrometry) • - photometric variability (can exploit, e.g. Cepheids, RR Lyrae) • - missing and censored data (unbiased: not a ‘pre-cleaned’ data set) • multidimensional iterative methods: • - cluster analysis, k-nn, neural networks, interpolation methods • required for astrometric reduction (identification of quasars, variables etc.) • maybe discovery of new types of objects  produce detailed classification catalogue of all 109 objects

4. Classification methodology

5. Minimum Distance Methods (MDM)  astrophysical parameter(s) d1,d2 data D distance to a template

6. Neural Networks

7. Parametrization example: RVS-like data CaII (849-874nm) data from Cenarro et al. (2001) R = 5700 (1/2 GAIA) SNR (median) = 70 (90% in range 20-140) Network trained on half and tested on other half Bailer-Jones (2003) blue = training data red = test data

8. Results: Teff and [Fe/H]

9. Classification issues • different data sensitivities to APs (Teff strong; [Fe/H] weak) • wide range of object types • inhomogeneous stellar models • hierarchical classifier • binary stars (raises dimensionality) • stellar variability • degeneracy • inhomogeneous data • calibration • etc.

10. CCD1b 1X CCD2 GAIA photometric systems 2B 6*Ag CCD3 Broad Band Photometer (BBP) • astrometric chromaticity correction • space for up to 7 bands • classification, Teff, extinction Medium Band Photometer (MBP) • AP determination • space for up to 16 bands Both photometric systems are still under development

11. Filter system evaluation • synthetic spectra: - BaSeL spectra (Lejeune et al. 1997) - wide range of Teff, logg, [Fe/H] • artifically redden: - Fitzpatrick (1999) extinction curves • GAIA photometric simulator + noise model (“photsim”) • split data set into two halves 1. for model training 2. for model evaluation

12. MBP performance estimates Accuracy varies a lot as a function of the 4 APs and magnitude Willemsen, Kaempf, Bailer-Jones (2003)

13. Heuristic filter design • objective: design filter system to maximally “separate” a set of stars • fixed parameters: set of stars, instrument, total integration time, Nfilters • free parameters: lc (central wavelength), Dl (width), f (fractional integration time), for each filter • maximize over the set of stars:

14. initialise population mutate filter system parameters simulate counts (and errors) from each star in each filter system select fitter filter systems (probability a fitness) calculate fitness of each filter system Evolutionary algorithm

15. HFD: a preliminary result nominal 10-band MBP-like system red = filter transmission x fractional integration time blue = CCD QE • high reproducibility (convergence) for given fixed parameters • broader filters produced that hitherto adopted in MBP design • substantial filter overlap • fitness higher than that of existing systems (e.g. 1X)