Code comparison
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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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Code comparison

Code comparison

ENZO

Hy Trac’s code

Renyue Cen’s code

GADGET

VERY SOON: ENZO/Trac-only analysis


Code comparison

Code comparison

Blue: Cen

Black: Trac

Denominator: ENZO


Code comparison

Code comparison


Code comparison

Code comparison


Code comparison

Thermal histories

Red: Cen

Black: Trac

Green: ENZO

Blue: GADGET


Code comparison

Dependence of

Cosmology result

On simulation type

(in analysis, we marginalized over the differences between 3 Cen simulations)


Code comparison

Code comparison


Code comparison

Mean absorption

Direct PCA analysis and power spectrum analysis of SDSS data agree, and agree with HIRES results.


Code comparison

PCA analysis of QSO spectra

Evolution of mean flux consistent with external constraints

No feature at z=3.2


Code comparison

Ly-alpha forest

SDSS quasar

spectrum

Cen simulation of the IGM (neutral hydrogen)

z = 3.7 quasar


Code comparison

Assumed cosmological

parameters

True cosmological

parameters

Theory (simulations)

Observations

Statistics (power spectrum)

Statistics (power spectrum)

Compare (chi^2)


Scales of various lss probes

Scales of various LSS probes

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(out of date figure by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)


Code comparison

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

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


Pre sdss lyaf power spectrum measurements

Pre-SDSS LyaF power spectrum measurements:

  • Croft et al. (1999)

    19 low resolution spectra

  • McDonald et al. (2000)

    8 Keck/HIRES spectra

  • Croft et al. (2002)

    30 Keck/HIRES, 23 Keck/LRIS spectra

  • Kim et al. (2004)

    27 VLT/UVES spectra


Sdss data

SDSS Data

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


Measured power

Measured Power

  • 2(k) = π-1 k P(k)

    (0.01 s/km ~ 1 h/Mpc)

  • Colors correspond to redshift bins centered at z = 2.2, 2.4, …, 4.2 (from bottom to top)

  • 1041<rest<1185 Å

  • Computed using optimal weighting

  • Noise subtraction

  • Resolution correction

  • Background subtraction using regions with rest>1268 Å

  • Error bars from bootstrap resampling

  • Code tested on semi-realistic mock spectra

  • HIRES/VLT data probes smaller scales

  • Computationally only modestly challenging


Fractional errors

Fractional Errors

  • Lines connect the fractional errors on PF(k) points

  • Equivalent to an overall amplitude measurement to +-0.6%

  • Logarithmic slope measurement to +-0.006


Noise power

Noise Power

  • Ratio of noise power to signal power

  • Important to subtract accurately, especially on small scales (in the future we won’t need noise subtraction because can cross-correlate multiple exposures)


Residual noise power

Residual Noise Power

  • Power in measured from differences between exposures of the same quasar

  • Should be zero

  • Actually consistent with a 16% underestimate of the noise subtraction term

  • Probably due to error in initial “gain”, maybe some sky subtraction noise


Bootstrap error estimates

Bootstrap error estimates

  • Bootstrap resampling by quasar

  • Tested using mock spectra

  • Diagonal errors reasonably close to Gaussian


Error correlations

Error Correlations

Inverted window function

Un-inverted window function


Resolution test

Resolution test

  • W2(k R) =

    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.


Background contamination

Background Contamination

  • The top set of lines shows the Ly forest power

  • The bottom set of lines shows the power in the region 1268<rest<1380Å


Background fraction

Background Fraction

  • Probably mostly metals (CIV), but not all.

  • Error bars starting at zero show error on the forest power.


Difference between two background estimates

Difference Between two Background Estimates

  • Difference in power between the regions 1268<rest<1380Å and 1409<rest<1523Å


Our simulations

Our Simulations

  • Predict PF(k) using simulations of a large grid in parameter space and compare directly to the observed PF(k).

  • Allow general relation PF(k) = f[PL(k)] (but only amplitude, slope, and curvature of PL(k)], no band powers).

  • IGM gas in ionization equilibrium with a not necessarily homogeneous UV background (still assuming homogeneous reionization).

  • Assume IGM not arbitrarily badly disturbed by feedback from galaxies (but allow for some winds).

  • Fully hydrodynamic simulations near the best-fit cosmological model are used to calibrate approximate hydro-PM simulations which are used to explore parameter space.

  • Marginalize over temperature density relation parameters, T=T0(1+)-1, mean absorption level, reionization history, etc.


Code comparison

HPM simulation grid


Code comparison

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


Best fitted model

Best fitted model

  • 2 ≈ 185.6 for 161 d.o.f.

  • A single model fits the data over a wide range of redshift and scale

  • Wiggles from SiIII-Ly cross-correlation

  • Helped some by HIRES data


Theory now includes

Theory now includes:

  • Rudimentary galactic superwinds (known to exist in starburst galaxies and LBGs)

  • Ionizing background fluctuations from quasars

  • Damped and lyman limit systems, which are not well modeled in simulations


Fluctuations in the ionizing background

Fluctuations in the ionizing background

  • Place quasars with a given luminosity function and lifetime in dark matter halos in a large (320 Mpc/h - Bode & Ostriker) N-body simulation (also try galaxies).

  • Compute the radiation field produced by the sources, including attenuation by the IGM. (Uros Seljak)

  • Fluctuations can be large at high redshift where the attenuation length is short.


Fluctuations in ionizing background

Fluctuations in ionizing background

Attenuation length is rapidly

decreasing with redshift,

so effect can be large at z>4,

negligible at lower redshifts


Fluctuations in ionizing background1

Fluctuations in ionizing background

Correlation of galaxies with density leads to coherent fluctions - suppression of power


Code comparison

Galactic winds heat IGM to 100,000K and pollute IGM with metals

Temperature maps

No wind

wind

Cen, Nagamine, Ostriker 2004


Code comparison

Neutral hydrogen maps show much less effect

No wind

wind


Code comparison

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


Code comparison

Effectively no effect from winds on the power spectrum


Damped and lyman limit systems

Damped and lyman limit systems

  • When density of hydrogen is high photons get absorbed and do not ionize hydrogen (self-shielding)

  • Simulations generally cannot simulate this accurately

  • We have measurements of the number density of these systems as a function of column density and redshift

  • We place these systems into densest regions of simulations

  • Damping wings (Lorenzians) wipe out a large section of the spectrum

  • This adds long wavelength power, removing it makes spectrum bluer

  • Important effect which was not previously estimated


Code comparison

Can determine power law slope of the growth factor to 0.1

Mandelbaum etal 2003


Comparison with theory first try

Comparison with theory (first try)

  • Curves from simulations

  • Fitted parameters: Amplitude and slope of the primordial power spectrum, mean absorption level, and temperature-density relation for the gas

  • 2 ≈ 192 for 106 degrees of freedom!


Siiii ly cross correlation bump

SiIII-Ly cross-correlation bump

  • SiIII absorbs at 1207 Å, corresponding to a velocity offset 2271 km/s

  • Vertical line at 2271 km/s

  • No other obvious bumps out to about 7000 km/s

  • Dashed line shows

    0.04 F(v-2271 km/s)/ F(0)


Best fitted model1

Best fitted model

  • 2 ≈ 185.6 for 161 d.o.f.

  • A single model fits the data over a wide range of redshift and scale

  • Wiggles from SiIII-Ly cross-correlation

  • Helped some by HIRES data


Code comparison

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


Model uncertainties

Model uncertainties

If potential systematic errors were ignored, errors would be a factor of 5 smaller!


Model uncertainties1

Model uncertainties

Uncertainties in the estimate of the noise and resolution of the SDSS data are allowed for


Model uncertainties2

Model uncertainties

Evolving cross-correlation between Lyman-alpha and SiIII absorption is included in the model (no change at this point)


Model uncertainties3

Model uncertainties

An evolving relation between temperature and density is included in the model (dotted line shows previous case)


Model uncertainties4

Model uncertainties

UV background fluctuations are included in the model


Model uncertainties5

Model uncertainties

Damping wings add power on large scales


Model uncertainties6

Model uncertainties

Fully hydrodynamic simulations include three different treatments of energy and metal feedback from galaxies


Model uncertainties7

Model uncertainties

Uncertainty in extrapolation of results from small-box simulations to larger scales


Model uncertainties8

Model uncertainties

Redshift evolution of the mean level of absorption is assumed to follow a power law in effective optical depth


Model uncertainties9

Model uncertainties

The overall normalization of the mean level of absorption is the most important nuisance parameter


Model uncertainties10

Model uncertainties

The order of adding parameters matters. Here we include only uncertainty in the mean absorption level


Cosmological parameters

Cosmological parameters

  • Observations:

    • WMAP

    • SDSS LyaF

    • HIRES LyaF (McDonald et al. 2000 observations)

    • SDSS galaxy clustering (Tegmark et al. 2003)

    • SDSS galaxy-galaxy lensing determination of bias (Seljak et al. 2004)

    • SN1a (Riess et al. 2004)

  • Parameters:

    • Always:

    • Sometimes:

  • MCMC to generate probability distributions (Alexey Makarov)


No evidence for departure from scale invariance n 1 dn dlnk 01

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


Basic six parameter model

Basic six parameter model

WMAP, Lya, SDSS gal (w/gg lensing

determination of bias), SN1a


Correlations with optical depth

Correlations with optical depth


Extension parameters one at a time

Extension parameters (one at a time)

(3 massive,

no SN1a)


Basic six parameter model1

Basic six parameter model


Basic six parameter model2

Basic six parameter model


Code comparison

Time evolution of equation of state

Individual parameters very degenerate


Time evolution of equation of state

Time evolution of equation of state

  • w remarkably close to -1

  • Robust against adding more terms

  • Best constraints at z=0.3

  • Lya helps because there is no evidence for dark energy at z>2


W is correlated with r

w is correlated with r


Code comparison

Parameter dependence of the power spectrum at z=3


Code comparison

Parameter dependence of the power spectrum at z=4

Early reionization leads to less small-scale power (more smoothing - Gnedin & Hui).


Code comparison

Parameter dependence of the power spectrum at z=2


Code comparison

High-z structure formation

  • Primordial power spectrum constraint

  • Mean level of absorption/ionizing background strength as a function of z

  • Ionizing background fluctuations

  • Smoothing (“Jeans”) scale of IGM

  • Temperature-density relation of IGM

  • Metal correlations

  • Galactic winds

  • Signatures of inhomogeneous reionization?

  • Other?


Remaining to be done for power spectrum cosmology

Remaining to be done for power spectrum cosmology

  • Winds from galaxies (better)

  • Inhomogeneous reionization (thermal history)

  • Alternative hydro codes

  • Anything else?


Code comparison

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


Ongoing future

Ongoing/Future

  • SDSS is an enormous source of information.

    • More spectra

    • Bispectrum

    • Correlation between absorption in pairs of quasars

    • Evolution of mean absorption level, PDF

    • Metal correlations

    • Lyman-beta auto/cross correlation

  • More high resolution spectra

  • 3D observing programs (baryon wiggles?)


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