gy ngyi kerekes e tv s l r nd university budapest n.
Download
Skip this Video
Download Presentation
Photometric parallax method

Loading in 2 Seconds...

play fullscreen
1 / 15

Photometric parallax method - PowerPoint PPT Presentation


  • 98 Views
  • Uploaded on

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 )

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Photometric parallax method' - kathleen-day


Download Now An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
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)
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.

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)

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
ad