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Photometric parallax methodPowerPoint Presentation

Photometric parallax method

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Photometric parallax method

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Gyöngyi Kerekes

Eötvös Lóránd University, Budapest

Photometricparallaxmethod

István Csabai

László Dobos

Márton Trencséni

MAGPOP 2008, Paris

- 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)

QUERY

TRAINING SET

Polinomial fit to main sequence:

Mr=f(r-i)

- 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

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

- 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

- No bright stars in SDSS!
- Observations with Photometric Telescope (50 cm) to calibrate SDSS stars to USNO stars
- Crossmatch with Hipparcos 117 stars

- 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.

Mr

r-i

SDSS Stripe 82 (Image coadd catalog with improved photometry)

~420,000 stars

Mr

Blue: ourestimation

Red: Juric, 2008

g-r

Applying Cartesian coordinate system:

Blue: our estimation

Red: Juric, 2008

Blue: Our estimation

Red: Juric, 2008

Z (pc)

Z (pc)

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

- Apply to all SDSS data
- Calculate metallicity
- Combine with kinematics (USNO, RAVE …)
- GALEX crossmatch