gal* : Tools to Model the PS1 Galaxy

Modeling the PS1 GalaxyMario Juric <[email protected]>, Tuesday, Aug 31st, 2010.

Pan-STARRS PS1 Collaboration Meeting, Queens University, Belfast, U.K.

gal*: Tools to Model the PS1 Galaxy

Mario JuricHarvard-Smithsonian Center for Astrophysics, Hubble Fellow



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.

DadMom

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)



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?



Obstacles

Observational Lack of data Largely resolved (SDSS, PS1)

Inferential Lack of capability (tools) to probabilistically

infer the underlying physical reality The primary obstacle



SDSS: Galactic Model Parameters

Disk + Inner Halo models

Z

R

Juric et al. (2008)



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)

Ground Truth Inference



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



SDSS: Galactic Model ParametersJuric et al. (2008)



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



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.



SDSS -> PS1 (GAIA, LSST, …)

1. Full forward-modeling of the observed datasets Inputs: model parameters Outputs: catalogs (to be compared w. real data)

2. Probabilistic (Bayesian) inference of model parameters Posteriors Evidence

Primarily a technical problem Code complexity and speed

galfa

stga

lfit



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



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



Example Outputs: Star Counts (in Shells of Apparent Magnitude)

r=15

r=29



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



galfast vs SDSS: b=50Juric et al. (in prep)

FG

K

M

HB

FG

K

M

HBWD WD

QSOs



CMR+Dust Map test: galfast vs SDSS @ b=0



Galactic Model Test: galfast vs SDSS @ l=60, b=45



Comprehensive Q/A



Sidenote: Bayesian Estimation of Stellar Parameters (galstar)

Uncertaintiesof parameterestimates



Sidenote: Bayesian Estimation of Distance and Extinction

ML estimate

Expectationvalue



Sidenote: Implementation

N-Body output

Analytic laws

Density cube

Monte Carlo draw of position, absolute

magnitude

[Fe/H] Photometry

3D extinction map

KinematicsAstrometry

Prop. Motion Multiplicity

Catalogs

Statistics

Posteriordensities

… additional postprocessing …

Observational errors

Inputs/Models Generator Postprocessing Output

galfast: schematic execution overview

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

Requirements:

1. Flexibility (arbitrary inputs and outputs)

2. Speed (GPU accelerated implementation)

Juric et al. (2010)



Speed

GPU speedup for 315 sq. deg. footprint, 0.5% photometry

0

50

100

150

200

250

T(C

PU

) / T(G

PU

) . Speedup

Runtime for 315 sq. deg. footprint, 0.5% photometry

0.01

0.1

1

10

100

1000

10000

100000

Tim

e (s

econ

ds)

.

GPU

CPU

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)



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

iesprobabilitn transitiox;x ofmatrix sparse :result

x,..,x,xx:stars of sampling

xx:star single a

N21

p

nnnn survey

survey



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



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 …



Adaptation to PS1: Photometric System Transformations

Eddie Schlafly



PS1 KP5: Wide AreaImage by Eddie Schlafly



KP5 Applications: Stellar Halo Populationsde Jongh et al. (2010)



KP5 Applications: Quantifying Halo SubstructureBell et al. (2008)



KP5 Applications: Quantifying Dwarf Selection Function



CFHT: Halo density profiles out to 35kpc

• 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

Sesar, MJ & Ivezic (subm.)



CFHT: Halo density profiles out to 35kpc

• 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

Sesar, MJ & Ivezic (subm.)



PS1 Analog: Medium-Deep Fields



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 parameters and density substructures Deep halo profiles (MDF+calib field (?) stacks) Mock PS1 catalogs

Soon: Disk density (stars+dust) model-free 3D mapping



Documents

gal* : Tools to Model the PS1 Galaxy