Die Vermessung der Milchstraße: Hipparcos, Gaia, SIM Vorlesung von Ulrich Bastian ARI, Heidelberg Sommersemester 2004

Gliederung 1.Populäre Einführung I: Astrometrie 2.Populäre Einführung II: Hipparcos und Gaia 3.Wissenschaft aus Hipparcos-Daten I 4.Wissenschaft aus Hipparcos-Daten II 5.Hipparcos: Technik und Mission 6.Astrometrische Grundlagen 7.Hipparcos Datenreduktion Hauptinstrument 8.Hipparcos Datenreduktion Tycho 9.Gaia: Technik und Mission 10.Gaia Global Iterative Solution 11.Wissenschaft aus Gaia-Daten 12.Sternklassifikation mit Gaia 13.SIM und andere Missionen

Sternklassifikation mit Gaia

Why we need object classification impact of astrometric/kinematic data only possible with astrophysical information GAIA has no input catalogue: objects not known a priori 2D colour cuts inefficient use of high dimensional data on board detection Global Iterative Solution requires “well- behaved” sources reliably identify QSOs for inertial reference frame responsibility of the GAIA Classification Working Group

Object classification/physical parametrization classification as star, galaxy, quasar, supernovae, solar system objects etc. determination of physical parameters: - T eff, logg, [Fe/H], [  /H], A( ), V rot, V rad, 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 10 9 objects

Top level classification system

Classification overview for more details see Bailer-Jones (2002)

Classification methodology

Minimum Distance Methods (MDM)  astrophysical parameter(s) d 1,d 2 data Ddistance to a template

Minimum Distance Methods (MDM)

Neural Networks

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

Results: T eff and [Fe/H]

Classification issues different data sensitivities to APs (T eff 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.

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

Filter system evaluation synthetic spectra: - BaSeL spectra (Lejeune et al. 1997) - wide range of T eff, 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

Example photometric results analysing and displaying multidimensional results is non-trivial... red = mean error (systematic error)blue = std. dev. about mean

Some blind testing results From ICAP-AB-003, Brown (2003)

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

Calibration GAIA is a deep survey: insufficient real templates exist......therefore use synthetic spectra –broad band colour predictions and absolute spectral fluxes do not always reproduce observations well –small differences are relatively reliable adopt a “self-calibration” approach –synthetic grid corrected with GAIA observations of a subset of stars with well-determined APs (based on ground based data) can retrain/reapply classifiers as new model spectra produced

Heuristic filter design objective: design filter system to maximally “separate” a set of stars fixed parameters: set of stars, instrument, total integration time, N filters free parameters: c (central wavelength),  (width), f (fractional integration time), for each filter maximize over the set of stars:

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

Fitness measure SNR distance of star r from neighbour n: = counts in filter i for star n= error in counts = difference in AP j between star n and r

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)