BEAST HackDay Intro (27 Nov 2017)

95bba3a98c20be969c4fb42ea31ae4ae?s=47 Karl Gordon
November 27, 2017

BEAST HackDay Intro (27 Nov 2017)

95bba3a98c20be969c4fb42ea31ae4ae?s=128

Karl Gordon

November 27, 2017
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  1. The BEAST Karl D. Gordon Space Telescope Sci. Inst. Baltimore,

    MD, USA BEAST hack day 27 Nov 2017 STScI
  2. The BEAST Bayesian Extinction And Stellar Tool • SED fitter

    for individual stars at a known distance • Include stellar and dust physics • Use Probabalistic/Bayesian techniques • Accurate noise model for crowded data • Must be fast – need to fit many stars – PHAT has >100 million stars
  3. Model of single star in a galaxy • The physical

    description of a stellar SED – 7 parameters give stellar and interstellar dust properties • Stellar evolutionary tracks – Birth mass, age, & metallicity – Mapping to radius, T(eff), & log(g) – Stellar atmospheres (surface flux) → T(eff) & log(g) • Dust Extinction curves – A(V) = Dust column; R(V) ~ average grain size – f A = mixing between dust w/ 2175 Å bump (Fitzpatrick 1999 ) and SMC Bar 2175 Å bumpless dust (Gordon et al. 2003 ) • The distance
  4. BEAST Strengths • Parameterized dust extinction model – Encompasses the

    known behavior – MW, LMC, SMC, & M31 • Noise model includes covariance – Correlation in noise between observed bands – From crowding/confusion and absolute calibration • Based on Bayesian techniques – Allows for the inclusion of prior information – Allows for hierarchical models (ensemble modeling)
  5. Visual BEAST SED Construction

  6. New Mixture Model encompassing all known Local Group Extinction Curves

    Gordon et al. (2015) and Gordon & Tchernyshyov (in prep.)
  7. Covariance! Covariance Matrix & Bias in Fitting Calculating Covariance &

    Bias from ASTs
  8. Priors = Other Knowledge = Assumptions

  9. Impact of Crowding/Confusion

  10. Visualization of Covariance Matrix & Bias from ASTs (from dedicated

    ASTs tests with 125 realizations per model)
  11. Visualization of 6-band covariance matrices (~2000 models x 20 realizations

    per model)
  12. BEAST Results PHAT Star in M31

  13. Recovery of Simulated Star

  14. None
  15. None
  16. None
  17. MegaBEAST • Multi-level model • Hierarchical Bayesian model • Ensemble

    model • Works by modeling the outputs of the BEAST – Mathematically equivalent to modeling the observed fluxes! – But only needs to model the BEAST fit parameters • Age, mass, metallicity • A(V), R(V), f A • Account for completeness and star/dust geometry, star formation history, ...
  18. None
  19. None
  20. Decomposed A(V) maps for Brick 21 Foreground M 31

  21. BEAST/MegaBEAST Effort • Technique paper – Gordon et al. (2016,

    ApJ, 826, 104) • Active team – Gordon, Arab, Boyer, Choi, Durbin, Fouesneau, Johnson, Kapala, Kahn, Sandstrom, Weisz, Williams, Yanchulova Merica-Jones • BEAST hack week (20-24 Feb 2017, UCSD) – Code development, testing code, documentation, etc. – Using astropy affiliated package template – Extensions for AGB and YSOs in dicussion • BEAST is fairly mature – public on github at the end of Feb 2017 – Setup for distributed development of code, documentation, etc. • MegaBEAST in development
  22. Thanks!