BEAST HackDay Intro (17 Jan 2020)

95bba3a98c20be969c4fb42ea31ae4ae?s=47 Karl Gordon
January 17, 2020

BEAST HackDay Intro (17 Jan 2020)

95bba3a98c20be969c4fb42ea31ae4ae?s=128

Karl Gordon

January 17, 2020
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  1. The BEAST Karl D. Gordon Space Telescope Sci. Inst. Baltimore,

    MD, USA BEAST hack day 17 Jan 2020 STScI
  2. The BEAST Bayesian Extinction And Stellar Tool • SED fitter

    for individual stars • 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. BEAST Math • Bayes’ Theorem – P(A|B) = P(B|A)P(A)/P(B) •

    P(θ|d) = P(d|θ)P(θ)/P(d) – θ = BEAST model parameters • Age, mass, metallicity, A(V), R(V), f A , distance • Observation model parameters: source density – d = data (fluxes) – P(θ) = priors – P(d) = constant for all stars (grid solution)
  20. MegaBEAST Math • P(θ|d) = P(d|θ)P(θ) = BEAST output •

    P(φ|d) = product[ int i { P(φ|θ)C(θ)(P(θ|d) i } ] – φ = MegaBEAST parameters • “meta” parameters • Star formation vs time, IMF, mass-metallicity, • log-normal parameters for A(V), R(V), f A • Distance function (Gaussian with width?) – P(φ|θ) = P(θ|φ)P(φ)/P(θ) = MegaBEAST computation • Adjustable priors • P(θ) = 1 (??) – C(θ) = completeness in θ space • From ASTs run from BEAST models – P(θ|d) = BEAST/P(θ) ∏∫ i P(ϕ∣θ)
  21. Results from the BEAST

  22. Results from the MegaBEAST

  23. None
  24. Decomposed A(V) maps for Brick 21 Foreground M 31

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

    ApJ, 826, 104) • Active team – Gordon, Boyer, Choi, Clark, Fouseneau, Goldman, Hagen, Johnson, Lindberg, Murray, Tollerud, Van De Putte, Williams, Wu • Regular BEAST Hack Days – Code development, testing code, documentation, etc. • BEAST is fairly mature – public on github – Distributed development of code, documentation, etc. • MegaBEAST in development
  26. FatBEAST 29 Dec 2017 • 1st official release! • Captures

    what was run on the PHAT data – 45 million stars • See BEAST paper for details
  27. DistantBEAST (v1.1) 28 Apr 2018 • Distance added as 7th

    variable • Optimized generation of ASTs inputs for toothpick noismodel • AST input locations based on surface brightness • Automated regression testing • Pip installable version • Docs, Bugfixes, etc.
  28. UnleashtheBEAST (v1.2) 22 Jun 2018 • ASTs based on source

    density or surface brightness • Model grid can be split – Allow for parallel execution – Resulting pDFS can be reassembled • Systemic velocity of galaxy added • Symlog for flux pPDFs allows negative values • Docs, Bugfixes, etc.
  29. HerdtheBEAST (v1.3.2) 5 Sep 2019 • AGB model atmospheres added

    • Generalized dust extinction curves • Simulated observations • run_beast.py split up (more pythonic) • Simple splinter noisemodel added • Python 2 support dropped • Docs, Bugfixes, etc.
  30. DevBEAST (v2.0) • Lots of script development – Batch files,

    xsede • Adjustable stellar priors • Docs, Bugfixes, etc.
  31. BEAST READY! • BEAST is ready for use • Priority

    is to write BEAST papers – Talk by Lea & github issue #333 • Improvements continuous, but major capabilities are there • Issues for work needed – Categorized by area and effort level
  32. MegaBEAST – DEV • Currently only solves for A(V) parameters

    • Internal mechanics setup for full solution • Need to expand to allow for fitting other parameters – R(V), f_A – Star formation history, metallicity history, IMF, etc. • Testing, testing, testing – Gain confidence that MegaBEAST working – Test versus MATCH (high priority for Scylla)
  33. Thanks!