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Advanced Stellar Populations

Advanced Stellar Populations. Raul Jimenez www.physics.upenn.edu/~raulj. Outline. Physics of stellar structure and evolution Synthetic stellar populations MOPED and VESPA. Light from galaxies. Is made of a collection of stars at different evolutionary stages

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Advanced Stellar Populations

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  1. Advanced Stellar Populations Raul Jimenez www.physics.upenn.edu/~raulj

  2. Outline • Physics of stellar structure and evolution • Synthetic stellar populations • MOPED and VESPA

  3. Light from galaxies • Is made of a collection of stars at different evolutionary stages • In galaxies we only see the integrated light

  4. Sloan Digital Sky Survey Largest data-set of galaxy spectra (about one million of them)

  5. Stellar populations models predict the integrated light of galaxies • Needs good stellar evolution models • Both interior and photosphere

  6. Basics of stellar evolution Time scales Dynamical tdyn ~ (G)1/2 ~ 1/2 hour for the Sun Thermal tth ~ GM2/RL ~ 107 years for the Sun Nuclear timescale tnuclear ~ 0.007qXMc2/L ~ 1010 years for the Sun Equations of Stellar Evolution Hydrostatic Equilibrium Energy Transport Energy Generation Remember that stars are simply balls of gas in (more-or-less) equilibrium

  7. Stars come with different Luminosities and Temperatures

  8. Evolution of stars

  9. Ingredients of synthetic stellar populations A good set of stellar interior models, in particular isochrones. A good set of stellar photosphere models From the above two build an isochrone A choice for the Initial Mass Function (If you know the sfh of the galaxy you know its metallicity history)

  10. Building an isochrone (not! trivial)

  11. Isochrones (continued) Horizontal branch

  12. Isochrones, do they resemble reality?

  13. How do the models compare among themselves?

  14. Fits are getting good nowadays

  15. 3AA Examples: Young Galaxy

  16. 3AA Examples: Old Galaxy

  17. Determining Star Formation History from Galaxy Spectra • Various indicators over spectral range • Broad spectral shape also contains information • Compare spectra from synthetic stellar population models with observed spectra

  18. Characterising the SFH • Current models and data allow the star formation rate and metallicity to be determined in around 8-12 time periods • 11 x 2 + 1 dust parameter = 23 parameters – significant technical challenge • To analyse the SDSS data would take ~200 years • Needs some way to speed this up by a large factor

  19. Lossless linear compression = probability of parameters given the data, if priors are uniform Assume: x = data μ= expected value of data, dependent on parameters (e.g. age) C = covariance matrix of data x → y = new (compressed) dataset Lossless? Look at Fisher Matrix

  20. Fisher Matrix Fisher matrix gives best error you can get: Marginal error on parameter θβ: σβ =√(F-1)ββ If Fisher Matrix for compressed data is same as for complete dataset, compression is (locally) lossless

  21. Characterising the problem

  22. e.g. fλ Linear compression methods Solve certain eigenvalue problem to make y uncorrelated, and B is chosen to tell you as much as possible about what you want to know.

  23. C known: MOPED* algorithm • Consider y1 = b1.xfor some MOPED (weight) vectorb1 Choose MOPED vector so that Fisher matrix element F11 is maximised(i.e. y1 “captures as much information as possible about parameter 1”) Solve generalised eigenvector problem Mb=Cb, where M=/1 (/1)T * Multiple Optimised Parameter Estimation and DatacompressionHeavens, Jimenez & Lahav, 1999, MNRAS, 317, 965

  24. Largest weights given to the x which are most sensitive to the parameter, and those which are least noisy. It decides. b1 C-1  1 Multiple parameters: • Construct y2=b2.xsuch that y2is uncorrelated with y1 • Maximise F22 • etc Massive compression (→ one datum per parameter). Completely lossless if C independent of 

  25. MOPED vectors

  26. Analytic fits for SSPs

  27. The mass function of SDSS galaxies over 5 orders of magnitude SDSS Panter et al. (2004) MNRAS 355, 764

  28. Comparison to the Millenium Run

  29. SFR in galaxies of diff. stellar masses Heavens et al. Nature 2004 • Split by mass Stellar masses: >1012 M๏… < 1010 M๏ Galaxies with more stellar mass now formed their stars earlier Curves offset Vertically for clarity (Curves offset vertically for clarity)

  30. The mass-metallicity relation 0.0 -0.5 Metallicity [Z/Zo] -1.0 8 9 10 11 12 Present stellar mass [Mo]

  31. More tests. This time systematics of SDSS and theoretical models have been included Models do matter IMF does not matter

  32. How well are we fitting?

  33. Where are the galaxies today that were red and blue in the past?

  34. To study environment use Mark Correlations (Connecting Stellar Populations and Correlations) • Treat galaxies not like points, but use attributes (e.g. luminosity) • Measure the spatial correlations of the attributes themselves • A mark is simply a weight associated with a point process (e.g. a galaxy catalogue) Sheth, RJ, Panter, Heavens, ApJL, astro-ph/0604581

  35. For example, use luminosity of galaxies

  36. SF as a function of environment (Mark Correlations) Sheth, RJ, Panter, Heavens, ApJL, astro-ph/0604581

  37. Metallicity as a function of environment (Mark Correlations) Sheth, RJ, Panter, Heavens, ApJL, astro-ph/0604581

  38. MCMC errors

  39. How many bins do I need?

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