Gy ngyi kerekes e tv s l r nd university budapest
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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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Gy ngyi kerekes e tv s l r nd university budapest

Gyöngyi Kerekes

Eötvös Lóránd University, Budapest

Photometricparallaxmethod

István Csabai

László Dobos

Márton Trencséni

MAGPOP 2008, Paris


Overview

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)


Estimating parallax and other physical parameters from colors

Estimatingparallaxandotherphysicalparametersfromcolors

QUERY

TRAINING SET


Juric et al 2008

Juricetal, 2008

Polinomial fit to main sequence:

Mr=f(r-i)


Our estimation method

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


Training set

TrainingSet

  • MILES library

  • INDO-US library

  • Bright stars from SDSS

  • M67

  • NGC 2420

  • Total number of stars: 3392


Miles and indo us spectra

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


Bright star catalogs

Brightstarcatalogs

  • No bright stars in SDSS!

  • Observations with Photometric Telescope (50 cm) to calibrate SDSS stars to USNO stars

  • Crossmatch with Hipparcos  117 stars


Open clusters from sdss

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.


Training set1

Trainingset

Mr

r-i


Preliminary results

Preliminaryresults

SDSS Stripe 82 (Image coadd catalog with improved photometry)

~420,000 stars

Mr

Blue: ourestimation

Red: Juric, 2008

g-r


Preliminary results1

Preliminaryresults

Applying Cartesian coordinate system:

Blue: our estimation

Red: Juric, 2008

Blue: Our estimation

Red: Juric, 2008

Z (pc)

Z (pc)


Sdss stripe 82 not coadd

SDSS Stripe 82 (notcoadd)


Preliminary results2

Preliminaryresults

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


Future works

Futureworks

  • Apply to all SDSS data

  • Calculate metallicity

  • Combine with kinematics (USNO, RAVE …)

  • GALEX crossmatch


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