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Simulating the 4% Universe Hydro-cosmology simulations and data analysis
Michael L. Norman SDSC/UCSD
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Lecture Plan
• Lecture 1: Hydro-cosmology simulations of baryons in the Cosmic Web – Lyman alpha forest (LAF) – Baryon Acoustic Oscillation (BAO)
• Lecture 2: Radiation hydro-cosmology
simulations of Cosmic Renaissance – Epoch of Reionization (EOR) – First Galaxies
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Cosmic Renaissance
1. First Stars
2. First Galaxies
3. Reionization
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When did reionization complete?
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Scientific Goals
• Connect reionization to first galaxies through direct numerical simulations
• Some Questions – How does reionization proceed? – Is the observed high-z galaxy population sufficient to
reionize the Universe? – How is galaxy formation and the IGM modified by
reionization? – How good are the analytic and semi-numerical models
of reionization?
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Three generations of cosmological reionization simulations
• 1. Local self-consistent – (small boxes < 10 Mpc) – CRHD+SF+ionization+heating – e.g., Gnedin 2000, Razoumov
et al. 2002
• 2. Global post-processing – (large boxes > 100 Mpc) – N-body + RT – e.g., Iliev et al. 2006
• 3. Global self-consistent – (large boxes > 100 Mpc) – CRHD+SF+ionization+heating – Norman et al. 2012, in prep.
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Post-processing Approach • Pioneered by Sokasian et al. (2003) and “perfected” by
Iliev, Shapiro, et al. (2006+) • Recipe:
– Perform high resolution N-body DM simulation in large volume (L>100 Mpc/h)
– Assign ionizing flux to every halo by some prescription – Post-process snapshots of the density field, sampled onto
a coarse grid, with a ray-tracing radiative transfer code, assuming baryons trace DM
– Sources and gas clumping factor “coarse grained” on the mesh
– No radiative feedback on source population or intergalactic gas
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Post-processing Approach • Key insights
– reionization proceeds from the “inside-out” (i.e., from overdense to underdense regions)
– reionization is “rapid” (∆z~2) • However
– redshift of overlap is not predicted, but can be “dialed in” since it depends critically on assumed (Mhalo/Lion) and fesc
– minimum halo mass cutoff a free parameter
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radiation background
galaxies IGM
photo-ionization photo-heating
absorption
feedback (energy, metals) SF-recipe
self-shielding photo-evaporation
infall
ionizing flux
multi-species hydrodynamics
radiative transfer
N-body dynamics
cosmic expansion self-gravity dark matter dynamics
baryonic sector
Self-Consistent Approach
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https://code.google.com/p/enzo
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What does “direct simulation” mean? • All physical processes are simulated at the same mass
and spatial resolution – DM, gas dynamics – parameterized star formation and feedbacks – radiation sources and transport – ionization/recombination/photoevaporation
• Only subgrid model is SF, which is calibrated to observations (Bouwens et al.)
• Advantage: sources and sinks of ionizing radiation and radiative feedback effects are simulated directly
• Disadvantage: very costly to bridge scales; some still missing (minihalos)
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Two Simulations Differing only in Volume ΛCDM, WMAP7
80 Mpc 32003 cells/particles
20 Mpc 8003 cells/particles
Run A and Run B have identical mass and spatial resolution, physics, ICs, etc.
Run A “¼ scale simulation”
Run B “Renaissance Simulation”
64x volume
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Mass and Spatial Resolution
• HMF complete to ~108 Ms to include dwarfs – Sets “minimal” mass
and spatial resolution – Mp = 5x105 Ms
– ∆x=25 ckpc • Simulate largest
volume possible with available computer resources
GOALS
Run B
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Numerical Methods • We use Enzo V2.1 in non-AMR mode
http://enzo.googlecode.com – 6 species fluid dynamics: PPM – Dark matter dynamics: Particle-Mesh – Gravity: FFTs
• Radiation transport: implicit flux-limited diffusion, coupled to gas ionization and energy equation (Reynolds et al. 2009)
• Star formation & SN feedback: modified Cen & Ostriker 92 with “distributed feedback” (Smith et al. 2011)
– Calibrated to Bouwens et al. (2011) SFRD
• UV radiative feedback: Pop II SED from Ricotti, Gnedin & Shull 2002
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Tests of Radiation Solver Reynolds et al. (2009)
• Correct I-front speeds are obtained even at low resolution due to implicit coupling of rad. transfer, ionization, and gas heating
Shapiro & Giroux ‘87 analytic test problem
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Results
• Run A (1/4 scale simulation) – Ionizing photons per H atom – Adequacy of MHR estimate
• Run B (Renaissance Simulation) – Role of large scale power – Suppression of star formation in low mass halos
due to radiative feedback
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z=12.5 z=9.2 z=8 z=7 z=6
t=362 Myr t=552 Myr t=664 Myr t=792 Myr t=969 Myr
ENZO radiation hydrodynamic cosmic reionization G. So, M. Norman, R. Harkness (UCSD), D. Reynolds (SMU)
Redshift/time evolution of density and temperature 8003/20 Mpc/512 core
density
temperature
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ENZO radiation hydrodynamic cosmic reionization M. Norman, R. Harkness, G. So (UCSD), D. Reynolds (SMU)
Redshift/time evolution of density and temperature 8003/20 Mpc/512 core
density
temperature
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Ionized Volume Fraction
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Photons per H atom
btw. 3.5-4.5 ionizing photons per H atom
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Visualizing “Inside-Out” Reionization: Z-reion Cube
• Every cell contains the redshift when it was first photo-ionized
• yt script: – Loop over all redshift
outputs (80) and test if fHII>0.9
– Uses nested parallel objects to divide up the work on 256 cores
– 56 sec on Gordon including IO
Z-reion
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Result
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Effective of Large Scale Power
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Effective of Large Scale Power
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Effect of large scale power
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HI going, going, gone….
Z=7 Z=6.5 Z=6.05
80 cMpc
Projected HI fraction Large-scale neutral patches before overlap
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Effect of large scale power
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Where is the star formation happening?
Z=7.3
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Where is the star formation happening?
Star formation strongly suppressed at Mh < 5x109 Ms
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Is this a resolution effect? NO
adiabatic hydro
SF + SN feedback
SF + SN feedback + radiative feedback
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Is this a resolution effect? NO Ra
tio o
f Hal
o G
as M
asse
s Depletion of baryons due to SN feedback Depletion of
baryons due to radiative feedback
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Visualizing Jeans Smoothing M. Norman, G. So, R. Harkness (UCSD), D. Reynolds (SMU)
Density fields from RHD and non-RHD models
z=8, RHD z=8, HD
Visualization by J. Insley (ANL) & R. Wagner (SDSC)
Z=8
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Visualizing Jeans Smoothing Normailzed density difference between RHD and non-RHD models
z=8, RHD z=8, HD
Visualization by J. Insley (ANL) & R. Wagner (SDSC)
Z=8
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Visualizing Jeans Smoothing Normailzed density difference between RHD and non-RHD models
z=8, RHD z=8, HD
Visualization by J. Insley (ANL) & R. Wagner (SDSC)
Z=8
non-radiative radiative no difference
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radiative density dist.
non-radiative density dist.
normalized density difference
ρ2-ρ1
ρ2+ρ1 yellow
red red
minus
ρ2
ρ1
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Jeans Smoothing
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Effect on Dark Matter Power
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SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
Cosmology simulation matter power spectrum measurement using vSMP
Source: Rick Wagner, Michael L. Norman. SDC. Used by permission. 2012
We have run two large (32003 uniform grid) simulations, with and without radiation hydrodynamics, to measure the effect of the light from the first stars on the evolution of the universe. To quantitatively compare the matter distribution of each simulation, we use radially binned 3D power spectra.
• 2 simulations • 32003 uniform 3D grids • 244GiB+ per field • 15k+ files each
Individual simulations
Power spectra
• Ran existing OpenMP-threaded code
• ~256GiB memory used • ~5 ½ hours per field • 0 development effort
Difference
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Summary: by the numbers • Direct RHD simulation of reionization now feasible in reasonably large
volumes • Reionization completes at z ~ 6 using the observed SFRD (Bouwens et
al. 2011) • Larger box begins reionization sooner, because of rare peaks, but
completes reionization at the same redshift (self-regulation?) • Full reionization requires ~ 4 photons/H atom • MHR formula provides a good estimator of when reionization will
occur provided global HII clumping factor is used (dense gas not excluded)
• Radiative feedback suppresses star formation in halos Mh < 5x109 Ms due to baryon depletion arising from Jeans smoothing
• Large-scale patches (>10 Mpc) of HI remain as late as z=5.8, which may be observable in LAE correlation function
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