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Photometric parallax method

Gyöngyi Kerekes Eötvös Lóránd University , Budapest. Photometric parallax method. István Csabai László Dobos Márton Trencséni. MAGPOP 2008, Paris. Overview. Estimate distances of stars  create 3 D maps Explore the structure of Milky Way Exponencial disks + power-law halo( es )

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Photometric parallax method

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  1. Gyöngyi Kerekes Eötvös Lóránd University, Budapest Photometricparallaxmethod István Csabai László Dobos Márton Trencséni MAGPOP 2008, Paris

  2. Overview • Estimate distances of stars  create 3D maps • Explore the structure of Milky Way • Exponencial disks + power-law halo(es) • Dwarf galaxies (merging) and streams • Our goal: reproduce current distributions / find new structures • Improvements in outer regions, giants • Gaia (launch around 2011)

  3. Estimatingparallaxandotherphysicalparametersfromcolors QUERY TRAINING SET

  4. Juricetal, 2008 Polinomial fit to main sequence: Mr=f(r-i)

  5. Ourestimationmethod • Non-parametric estimator • We use all magnitudes (colors) from SDSS • Nearest neighbors of a point in a 5D space • Weight the estimated parameters with an exponencial distribution • Can be adopted to other photometric systems

  6. TrainingSet • MILES library • INDO-US library • Bright stars from SDSS • M67 • NGC 2420 • Total number of stars: 3392

  7. MILES and INDO-US spectra • These libraries were targeted to stars with different stellar parameters • Synthetic magnitudes • Crossmatchedwith Hipparcos catalog • Challenges: • wavelength coverage ofspectra is not enough • normalization of syntheticmagnitudes MILES+BaSeL

  8. Brightstarcatalogs • No bright stars in SDSS! • Observations with Photometric Telescope (50 cm) to calibrate SDSS stars to USNO stars • Crossmatch with Hipparcos  117 stars

  9. Openclustersfrom SDSS • First chosen as test objects • Turned out at estimation of distances that giants are overrepresented in the training set • After applying distance modulus from (Harris et al, 1996)  we added them to the TS. (1): Anthony-Twarog et al, 2006 (2): An et al, 2007 b.

  10. Trainingset Mr r-i

  11. Preliminaryresults SDSS Stripe 82 (Image coadd catalog with improved photometry) ~420,000 stars Mr Blue: ourestimation Red: Juric, 2008 g-r

  12. Preliminaryresults Applying Cartesian coordinate system: Blue: our estimation Red: Juric, 2008 Blue: Our estimation Red: Juric, 2008 Z (pc) Z (pc)

  13. SDSS Stripe 82 (notcoadd)

  14. Preliminaryresults • Stripe 82 in SDSS (~420,000 stars)

  15. Futureworks • Apply to all SDSS data • Calculate metallicity • Combine with kinematics (USNO, RAVE …) • GALEX crossmatch

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