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Age determination using AnalySED (multi-dimensional SED analysis) Method (data-analysis part)

Age determination using AnalySED (multi-dimensional SED analysis) Method (data-analysis part). Zhaoyu Zuo 2008-11-06. outline. M83 and data archive SED method Data-analysis steps Proto-type results Some other considerations. About M83. southern grand-design galaxy

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Age determination using AnalySED (multi-dimensional SED analysis) Method (data-analysis part)

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  1. Age determination using AnalySED (multi-dimensional SED analysis) Method (data-analysis part) Zhaoyu Zuo 2008-11-06

  2. outline • M83 and data archive • SED method • Data-analysis steps • Proto-type results • Some other considerations

  3. About M83 • southern grand-design galaxy • Metal-rich(>Z_solar) Sc galaxy • Almost face-on (Sofue et al. 1999) • Nearby galaxy, 4.5 Mpc (Thim et al. 2003; Karachentsev 2005) • star-formation rate: ~5 M_solar/yr (Kennicutt 1998)

  4. Data archive • SWIFT-UVOT: pixel scale: 0.5”/pixel. telescope PSF: 2.0”@350 nm band — exposure time — :comments V — 307+347s B — 85s: age indicator [The exposure is a little shallow] U — 190+48s: age indicator UVW1 — 1051s UVM2 — 2866s UVW2 — 2288+1545s • 2mass: pixel scale: 1”/pixel. telescope PSF: 2.0” J-band: Ks-band: as template to remove old stellar population contamination.

  5. How to break age-metallicity-extinction degeneracy [1] U and B bands are vital for age determination. [2] UV band can break age-extinction degeneracy [3] NIR band is crucial for breaking age-metallicity degeneracy. UV-Optical-NIR Combination SWIFT/UVOT 2MASS

  6. Pixel-by-pixel method • [1] these multi-band images should be well-aligned, that’s to say, each pixel in each image samples the same location in the galaxy. • [2] a common pixel-scale between these image bands(greater than the worst PSF, 2 arcsec) (Conti et al., 2003; Eskridge et al., 2003; Lanyon-Foster et al., 2007; Kassin et al., 2003; Welikala et al., 2007).  cast all the images into 5”/pixel (~110pc).

  7. Simple stellar population model(Starburst99) • Specifically developed for the evolutionary synthesis analysis of populations of massive stars, and are best suited to the conditions typically found in starburst environments. [1] include critical phases in stellar evolution, such as the red supergiant (RSG) phase, Wolf-Rayet stars, and the thermally-pulsing AGB (TP-AGB) phase [2] include observational high-resolution UV spectra, to allow for the analysis of stellar and interstellar absorption lines and line profiles at various metallicities. [3] include gaseous emission lines in the models in a simplified fashion; its contribution becomes important when hot stars providing ionising photons (and thus line emission) are present

  8. Method • We construct SEDs from models by folding the spectra with a large number of filter response functions to obtain absolute magnitudes.

  9. Method • we use the chi-square minimum method to compare the observation and the model. • we add statistical noise to the observed magnitudes, and the errors are drawn from a Gaussian distribution with the Gaussian sigma corresponding to the ’observational’ uncertainties. • we then repeat the procedure for 1000 times in the given SED library, then we can get 1000 best-fitting models and the associated parameters. • we use the median value as the true value, The uncertainties are centered around the median solution; they serve as equivalents to the 1sigma standard deviation around the average values.

  10. Photometry and uncertainty • photometry is calculated using: • The combined uncertainty is derived from the following relation:

  11. Contamination • foreground star contamination adopted from 2MASS point source catalog • sky background contamination We use IRAF msky tool to get the sky background and 1sigma error for all the images. Then we subtract the value from the images. • old stellar population contamination

  12. old stellar population contamination subtraction • All what we want to do is to examine the contribution of old stellar components in different bands, and now we should remove the contribution of young stellar components (traced by UV band) from the image.

  13. old stellar population contamination subtraction steps • Firstly we cast images into the same pixel-scale (5 arcsec/pixel) • We choose the galaxy center from NASA/IPAC Extragalactic Database (NED) as the center of the annulus: Equatorial (J2000.0) (204.2539583, -29.8654167) 3) The annulus radius is 5arcsec, from 5arcsec to 400arcsec. 4) We calculate the intensity ratio (F_uv/F_k) using pixel-by-pixel method firstly, then we can get the median ratio profile using a median value in each annulus

  14. Ratio(F_uw2/F_k)*F_k

  15. The errors of the regions we are most interested in are within 0.1mag

  16. Age map The age uncertainty is mostly within 30Myr.

  17. Extinction map

  18. We need consider… • How to estimate old stellar population contamination better?  GALFIT (Peng et al. 2002) has the ability to produce a model galaxy (axisymmetric components) based on the best-fitting parameters • Large chi^2 value?  (1) the wrong photometry? (2) multi-age components? • Other method to estimate the age uncertainty? (some numerical tricks?) • Accessorial method to determine the young age? For example, Halpha image data? • We also need to do the calibration with other age determine method.

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