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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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Gyöngyi Kerekes

Eötvös Lóránd University, Budapest

## Photometricparallaxmethod

István Csabai

László Dobos

Márton Trencséni

MAGPOP 2008, Paris

### 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)

QUERY

TRAINING SET

### Juricetal, 2008

Polinomial fit to main sequence:

Mr=f(r-i)

### 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

### TrainingSet

• MILES library

• INDO-US library

• Bright stars from SDSS

• M67

• NGC 2420

• Total number of stars: 3392

### 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

### Brightstarcatalogs

• No bright stars in SDSS!

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

• Crossmatch with Hipparcos  117 stars

### 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.

Mr

r-i

### Preliminaryresults

SDSS Stripe 82 (Image coadd catalog with improved photometry)

~420,000 stars

Mr

Blue: ourestimation

Red: Juric, 2008

g-r

### Preliminaryresults

Applying Cartesian coordinate system:

Blue: our estimation

Red: Juric, 2008

Blue: Our estimation

Red: Juric, 2008

Z (pc)

Z (pc)

### Preliminaryresults

• Stripe 82 in SDSS (~420,000 stars)

### Futureworks

• Apply to all SDSS data

• Calculate metallicity

• Combine with kinematics (USNO, RAVE …)

• GALEX crossmatch