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Bayesian Analysis of Stellar Evolution

Bayesian Analysis of Stellar Evolution. Team Members: Steve DeGennaro (UT) Elizabeth Jeffery (STScI) Bill Jefferys (UT, UVM) Nathan Stein (Harvard) David van Dyk (UC, Irvine) PI: Ted von Hippel (Siena, UT). EuroWD2010, Tubingen August 16-20, 2010. from Hugh Harris:

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Bayesian Analysis of Stellar Evolution

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  1. Bayesian Analysis of Stellar Evolution Team Members: Steve DeGennaro (UT) Elizabeth Jeffery (STScI) Bill Jefferys (UT, UVM) Nathan Stein (Harvard) David van Dyk (UC, Irvine) PI: Ted von Hippel (Siena, UT) EuroWD2010, Tubingen August 16-20, 2010

  2. from Hugh Harris: “160 WDs with new or improved USNO parallaxes that will be in our next paper (Dahn et al AJ 2011, almost completed)”

  3. Bayesian Approach • Write Bayes equation for this problem, cannot analytically integrate it • Simulate star clusters, binaries, single white dwarfs using isochrones, IFMR, WD cooling and atmosphere models, then recover input parameters using MCMC • Markov chain Monte Carlo constructs a draw from the posterior distributions that are used to infer the quantities of interest (stellar masses, ages)

  4. [Fe/H]  dist  dust  0.9 0.5

  5. ? ?

  6. +

  7. + MWD crystal MWD cooling precursor ages

  8. star A A+B star B

  9. Das Ende

  10. Bayesian Inference posterior distribution α likelihood * prior or p(model|data) = p(data|model) * p(model) / p(data) with Θ= model or model parameters p(data) = ∫ p(data| Θ) p(Θ) d Θ → Markov Chain Monte Carlo

  11. Theory: Ingredients • IMF (e.g., Miller & Scalo 1979) • pre-WD evolution (e.g., Girardi et al. 2000) • Initial-Final Mass Relation (e.g., Weidemann 2000) • WD cooling time scales (e.g., Wood 1992) • WD atmosphere colors (e.g., Bergeron et al. 1995) • binaries, non-DA/DA ratio, Mwd,up • other variants on the above

  12. NGC 188 Sarajedini et al. 1999

  13. An Example 1.0 0.8 0.3

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