Code comparison. ENZO Hy Trac’s code Renyue Cen’s code GADGET. VERY SOON: ENZO/Trac-only analysis. Code comparison Blue: Cen Black: Trac Denominator: ENZO. Code comparison. Code comparison. Thermal histories Red: Cen Black: Trac Green: ENZO Blue: GADGET. Dependence of
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ENZO
Hy Trac’s code
Renyue Cen’s code
GADGET
VERY SOON: ENZO/Trac-only analysis
Code comparison
Blue: Cen
Black: Trac
Denominator: ENZO
Code comparison
Code comparison
Thermal histories
Red: Cen
Black: Trac
Green: ENZO
Blue: GADGET
Dependence of
Cosmology result
On simulation type
(in analysis, we marginalized over the differences between 3 Cen simulations)
Code comparison
Mean absorption
Direct PCA analysis and power spectrum analysis of SDSS data agree, and agree with HIRES results.
PCA analysis of QSO spectra
Evolution of mean flux consistent with external constraints
No feature at z=3.2
Ly-alpha forest
SDSS quasar
spectrum
Cen simulation of the IGM (neutral hydrogen)
z = 3.7 quasar
Assumed cosmological
parameters
True cosmological
parameters
Theory (simulations)
Observations
Statistics (power spectrum)
Statistics (power spectrum)
Compare (chi^2)
The Ly forest is great for determining the running of the spectral index, ,
because it extends our knowledge to small scales
We only report an amplitude and slope no band powers
(out of date figure by
Max Tegmark)
Constraints in the natural LyaF plane from WMAP, minimal model, with and without running
No evidence for departure from scale-invariance n=1, dn/dlnk=0
3-fold reduction in errors on alpha_s
Very large running ruled out
19 low resolution spectra
8 Keck/HIRES spectra
30 Keck/HIRES, 23 Keck/LRIS spectra
27 VLT/UVES spectra
3300 spectra with zqso>2.3 (DR3 has 5767)
redshift distribution of quasars
1.4 million pixels in the forest
redshift distribution of Ly forest pixels
(0.01 s/km ~ 1 h/Mpc)
Inverted window function
Un-inverted window function
exp[-(k R)2]
I measured the power in the sky spectra near the 5577 Å line (a delta function), and divided by the resolution estimate.
HPM simulation grid
Nuisance parameters
Errors +-0.01 on both parameters if modeling uncertainty is ignored:
Noise/resolution
Mean absorption
Temperature-density
Damping wings
SiIII
UV background fluctuations
Galactic winds
reionization
Attenuation length is rapidly
decreasing with redshift,
so effect can be large at z>4,
negligible at lower redshifts
Correlation of galaxies with density leads to coherent fluctions - suppression of power
Galactic winds heat IGM to 100,000K and pollute IGM with metals
Temperature maps
No wind
wind
Cen, Nagamine, Ostriker 2004
Neutral hydrogen maps show much less effect
No wind
wind
Strong wind versus no wind simulations
Winds have no effect after simulations have been adjusted for temperature change
This is not conclusive and more work is needed to investigate other possible wind models
Effectively no effect from winds on the power spectrum
Can determine power law slope of the growth factor to 0.1
Mandelbaum etal 2003
0.04 F(v-2271 km/s)/ F(0)
Self calibration
Errors +-0.01 on both parameters if modeling uncertainty is ignored:
Noise/resolution
Mean absorption
Temperature-density
Damping wings
SiIII
UV background fluctuations
Winds
reionization
If potential systematic errors were ignored, errors would be a factor of 5 smaller!
Uncertainties in the estimate of the noise and resolution of the SDSS data are allowed for
Evolving cross-correlation between Lyman-alpha and SiIII absorption is included in the model (no change at this point)
An evolving relation between temperature and density is included in the model (dotted line shows previous case)
UV background fluctuations are included in the model
Damping wings add power on large scales
Fully hydrodynamic simulations include three different treatments of energy and metal feedback from galaxies
Uncertainty in extrapolation of results from small-box simulations to larger scales
Redshift evolution of the mean level of absorption is assumed to follow a power law in effective optical depth
The overall normalization of the mean level of absorption is the most important nuisance parameter
The order of adding parameters matters. Here we include only uncertainty in the mean absorption level
No evidence for departure from scale-invariance n=1, dn/dlnk=0
3-fold reduction in errors on alpha_s
Very large running ruled out
WMAP, Lya, SDSS gal (w/gg lensing
determination of bias), SN1a
(3 massive,
no SN1a)
Time evolution of equation of state
Individual parameters very degenerate
Parameter dependence of the power spectrum at z=3
Parameter dependence of the power spectrum at z=4
Early reionization leads to less small-scale power (more smoothing - Gnedin & Hui).
Parameter dependence of the power spectrum at z=2
High-z structure formation
Is the result correct?
To spoil the result the possible systematic must have very specific properties:
Must boost power on large scales in such a way to still give consistent slope derivative (ie, the results are consistent on large and small scales) and change slope and amplitude in a very specific way
Splits by redshift and scale give consistent results (one may imagine the systematic to be significantly redshift dependent between z=2-4 and to be more important on large or small scales); we see the same power spectrum