gliederung
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
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Gliederung
1. Populäre Einführung I: Astrometrie2. Populäre Einführung II: Hipparcos und Gaia3. Wissenschaft aus Hipparcos-Daten I 4. Wissenschaft aus Hipparcos-Daten II5. Hipparcos: Technik und Mission6. Astrometrische Grundlagen 7. Hipparcos Datenreduktion Hauptinstrument8. Hipparcos Datenreduktion Tycho9. Gaia: Technik und Mission10. Gaia Global Iterative Solution11. Wissenschaft aus Gaia-Daten12. Sternklassifikation mit Gaia13. SIM und andere Missionen
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Sternklassifikation mit Gaia
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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
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Classification methodology
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Minimum Distance Methods (MDM)
astrophysical parameter(s)d1,d2 dataD distance to a template
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Neural Networks
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Parametrization example: RVS-like data
blue = training datared = test 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)
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Results: Teff and [Fe/H]
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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.
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GAIA photometric systems
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
6*Ag
CCD3
2B 1X CCD1b CCD2
Both photometric systems are still under development
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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
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MBP performance estimates
mag dex dex %
~0.1 0.05‒0.25 0.20.1‒0.35
0.7 8
~1.0 8
0.08 0.4 0.03 4
0.3 0.35
Av) ([Fe/H]) (logg) (Teff)
KV, G=15, Av = 0, [Fe/H] = +0.1..-2 1–2KV, G=20, Av = 0 0.1‒0.7 0.3–0.5 2–5KV, G=20, Av = 6
KIII, G=15, Av = 0 0.2–0.4 0.2–0.3 2.5–4KIII, G=15, Av = 6 0.7–0.8
AIII, G=15, Av = 0, [Fe/H] ~ 0
BV, G=15, Av = 6, [Fe/H] ~ 0
Accuracy varies a lot as a function of the 4 APs and magnitude
Willemsen, Kaempf, Bailer-Jones (2003)
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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: c (central wavelength), (width), f (fractional integration time), for each filter
• maximize over the set of stars:
fitness ~SNR separationAP difference
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Evolutionary algorithm
initialise population
simulate counts (and errors) from each star in each filter system
calculate fitness ofeach filter system
select fitter filter systems(probability a fitness)
mutate filter systemparameters
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HFD: a preliminary resultnominal 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)