gal* : Tools to Model the PS1 Galaxy

1. gal*: Tools to Model the PS1 Galaxy Mario Juric Harvard-Smithsonian Center for Astrophysics,Hubble Fellow TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: AAAAAAAAAA

2. About the Author • Mario Jurić • Institute for Theory and Computation, Harvard/CfA • Interests: High Data Volume Astronomy (Surveys) • Galactic structure,formation, and evolution • Projects: • SDSS • PS1 (KP5) • LSST (MWL&V, ImSim) • This talk: Tools forMW structure sciencewith PS1. Mom Dad

3. The Milky Way Components: Fingerprints of Formation and a Laboratory for Dynamics • Thin disk (gas acc., mergers) • Thick disk (merger history, secular evolution) • Bulge and bar (merger history, secular evolution) •  Stellar halo (early formation, history of assembly) •  Galactic center Globular clusters (formation, dynamics) The Dark Matter halo Milky Way satellite system (MW assembly, galaxy formation, dark matter properties)

4. Reconstructing Galactic Formation and Evolution Name of the game: measuring the number, normalizations, shapes and histories of Galactic components (including MW satellites). How many pieces, which piece came from where and when, and where to look for the most interesting (usually: the oldest) piece?

5. Obstacles • Observational • Lack of data • Largely resolved (SDSS, PS1) • Inferential • Lack of capability (tools) to probabilistically infer the underlying physical reality • The primary obstacle

6. Z R SDSS: Galactic Model Parameters Juric et al. (2008) Disk + Inner Halo models

7. Unrecognized Multiplicity • An unresolved multiple system mistaken for a single star • Luminosity changes, color (approx.) does not • Error in distance estimate • Early types: >60% (Duquennoy & Mayor 1991) • Late types: 20-40% (Reid et al. 2006; Reid & Gizis 1997; Fischer & Marcy 1992) Inference Ground Truth

8. SDSS: Unrecognized Multiplicity • An unresolved multiple system mistaken for a single star • Luminosity changes, color (approx.) does not • Error in distance estimate • Early types: >60% (Duquennoy & Mayor 1991) • Late types: 20-40% (Reid et al. 2006; Reid & Gizis 1997; Fischer & Marcy 1992) • Possible to statistically correct for, if the binary fraction is known Effect of binarity on derived model parameters

9. SDSS: Galactic Model Parameters Juric et al. (2008)

10. SDSS: Disk Model Likelihood Surfaces X-Sections • Best fit: • Z0 = 25 pc • H1=245 pc, H2=740 pc • L1=2.15 kpc, L2=3.3 kpc • f=13% • Reduced c2=1.6 • Strong covariance between individual parameters

11. SDSS: Halo Model Likelihood Surfaces X-Sections • Inner halo • nH = 2.8 • qH = 0.6 • fH = 0.5%, • Obtaining full posteriors rises in importance as we begin examining the contributions of more tenuous components (accreted vs. in situ halo, metal weak thick disk, etc.) • Especially when contamination due to imperfect star-galaxy separation is taken into account.

12. SDSS -> PS1 (GAIA, LSST, …) • Full forward-modeling of the observed datasets • Inputs: modelparameters • Outputs: catalogs (to be compared w. real data) • Probabilistic (Bayesian) inference of model parameters • Posteriors • Evidence • Primarily a technical problem • Code complexity and speed galfast galfit

13. galfast – fast Galactic model sampler • A realistic simulation of the observed N-D (stellar) sky (density, kinematics, abundances, …) • Inputs: (arbitrary) input models (density, kinematics, dust, …), and stellar-parameter-magnitude relations (e.g. isochrones). Observational system definition (obsv. errors) • Outputs: mock catalogs, counts, density maps, likelihoods • Basic algorithm: sampling from a multidim. space of (X, Y, Z, absmab, Fe/H, …) over the survey volume (PS1: ~1011 samples) • Simple, trivially parallelizable, and computationally expensive

14. Inputs • Color-Magnitude relations (CMRs) • luminosity-metallicity-color (SDSS bands) relations for MS+RGB (empirical calibrations), H+He WDs (Bergeron models), RR Lyrae (empirical), BHB stars (empirical) • 3D dust maps • 3D data cube • Amores & Lepine (2005) exponential + small-scale clumpiness to asymptote to SFD’98 at infinity • Stellar Number Density • Exponential disk(s), power law halos, or a 3D data cube (e.g., N-body simulation result) • Metallicity • Ivezic et al. (2008) model • Kinematics • Bond et al. (2010) model

15. Example Outputs: Star Counts (in Shells of Apparent Magnitude) r=15 r=29

16. CMR+Dust Map test: galfastvs SDSS @ b=50 Juric et al. (in prep)

17. galfastvs SDSS: b=50 Juric et al. (in prep) M M K K G G QSOs F F WD WD HB HB

18. CMR+Dust Map test: galfastvs SDSS @ b=0

19. Galactic Model Test: galfastvs SDSS @ l=60, b=45

20. Comprehensive Q/A

21. Sidenote: Bayesian Estimation of Stellar Parameters (galstar) Uncertaintiesofparameterestimates

22. Sidenote: Bayesian Estimation of Distance and Extinction Expectationvalue ML estimate

23. Sidenote: Implementation Juric et al. (2010) A really fast direct 4D PDF sampler: r(X, Y, Z, M) or r(l, b, DM, M) Stellar properties given as P(prop|X,Y,Z,M) and assigned in postprocessing galfast: schematic execution overview Inputs/Models Generator Postprocessing Output [Fe/H] Photometry Analytic laws Catalogs Monte Carlo draw of position, absolute magnitude Astrometry Kinematics Statistics Prop. Motion Multiplicity N-Body output … additional postprocessing … Posteriordensities Density cube 3D extinction map Observational errors Requirements: Flexibility (arbitrary inputs and outputs) Speed (GPU accelerated implementation)

24. Speed Tesla S1070 (single GPU) vs. Xeon E5405 2.0GHz (single core) For photometric precision ~0.005mag: ~240x speedup Depending on the requested level of realism and outputs, can generate a mock (oversampled) PS1 in <10 hrs. (Jan2010 AAS poster)

25. Work in Progress: galfit • Even with a fast generator like galfast, it’s unfeasible to run it for every likelihood computation • Instead, record the scattering probability matrices from a single run: • Compute subsequent models without going through the Monte Carlo stage • Will allow us to compute posterior probabilities for the full PS1 stellar dataset galfit galfast

26. Code: mwscience.net/galfast (the usual PS1 ps1sc pass.)

27. Data Products: Mock Catalogs • Mock PS1 catalogs • Mocks with known ground truth that is as close as possible to the real Galactic model • PS1 Footprints, flux limits, photometric errors, completeness, masking, … • We will begin producing these as soon as the above are assessed. • Uses: • Optimizing candidate selection algorithms (dwarf galaxies, streams, brown dwarfs…) • Estimating selection functions • …

28. Adaptation to PS1: Photometric System Transformations Eddie Schlafly

29. PS1 KP5: Wide Area Image by Eddie Schlafly

30. KP5 Applications: Stellar Halo Populations de Jongh et al. (2010)

31. KP5Applications: Quantifying Halo Substructure Bell et al. (2008)

32. KP5Applications: Quantifying Dwarf Selection Function

33. CFHT: Halo density profiles out to 35kpc Sesar, MJ & Ivezic (subm.) • Solid: CFHTLS data • Dashed: Juric et al. (2008) c/a=0.64 oblate power-law halo • Note: J08 models fitted to D<15kpc halo • Fairly good agreement for W3 (north) and W4 (south) fields for D<20kpc • Deviation at large distances

34. CFHT: Halo density profiles out to 35kpc Sesar, MJ & Ivezic (subm.) • q=0.7, n=-2.6 inner profile • q=0.7, n=-3.8 outer profile • transition at Rbreak ~ 28kpc • no evidence for triaxiallity • no evidence for change of oblateness

35. PS1 Analog: Medium-Deep Fields

36. Summary & Outlook (~next 6 months) • gal*: A set Galaxy modeling tools for PS1 • Currently being applied to SDSS & CFHT • Calibration, calibration, calibration! • Nearly everyone is interested in this, efforts should be coordinated • PS1 Test #1: Repeat Sloan • Same area, same tools -> same results. • Galactic structural parametersand density substructures • Deep halo profiles (MDF+calib field (?) stacks) • Mock PS1 catalogs • Soon: Disk density (stars+dust) model-free 3D mapping