WMAP-5 data: foreground reduced I maps and their SYNFAST sim
Posted: February 26 2009
Hi,
I'm going to analyze large-scale characteristics of the CMB and currently trying to get familiar with data evaluation and its simulation by SYNFAST.
I'm using the WMAP-5 foreground reduced temperature maps per frequency band, available on LAMBDA (e.g. wmap_band_forered_iqumap_r9_5yr_V_v3.fits). Thereby, I observed something strange when comparing the map to Gaussian simulations. To get started, I wanted to compare the rms temperature fluctuations on large scales of real maps with their simulations.
First of all, I multiply a map (e.g. the V map) by the appropriate mask for temperature analysis (wmap_ext_temperature_analysis_mask_r9_5yr_v3.fits, also on LAMBDA) and degrade it to Healpix resolution 8 (via the Healpix fortran routine UD_GRADE). Now, the map is pixelized on a scale of about 7 degrees.
Then, I calculated the map's rms temperature fluctuation on this scale, just by averaging the square of the temperature fluctuations delta_T in each pixel over the whole map and taking the square-root in the end.
This value is to be compared to Gaussian simulations, created by the Healpix facility SYNFAST based on the WMAP-5 best fit Cl spectrum (e.g. computed by CAMB with the parameter file given by LAMBDA, see below). I expected the following: Since the best-fit Cls originate from the true WMAP-maps, my real rms temperature fluctuation should be (nearly) the same as the mean value of many Gaussian simulations running through the same masking and degrading procedure.
But that is not what I observe!
The mean value of the rms temperature fluctuations of a bunch of Gaussian simulations is about a factor of 1.4 too large.
I cannot find any explanation for this discrepancy. The instrument noise can be neglected on this large scale and could only correct into the wrong direction, I guess.
Do I understand something wrong or can anybody see a mistake?
Any hints are welcome!
Cheers,
Maik
If it helps you, here is the parameter fie for CAMB as downloaded from LAMBDA:
#########################################################
output_root = bestfit
get_scalar_cls = T
get_vector_cls = F
get_tensor_cls = F
COBE_normalize = F
CMB_outputscale = 7.425625e12
get_transfer = T
do_nonlinear = 0
l_max_scalar = 2000
k_eta_max_scalar = 4000
do_lensing = T
lensing_method = 1
w = -1
cs2_lam = 1
hubble = 0.724365E+02
use_physical = T
ombh2 = 0.226823E-01
omch2 = 0.108085E+00
omnuh2 = 0
omk = 0
temp_cmb = 2.725
helium_fraction = 0.24
massless_neutrinos = 3.04
massive_neutrinos = 0
nu_mass_eigenstates = 1
nu_mass_degeneracies = 0
nu_mass_fractions = 1
transfer_high_precision = T
transfer_kmax = 2
transfer_k_per_logint = 0
transfer_num_redshifts = 1
transfer_interp_matterpower = T
transfer_power_var = 7
transfer_redshift(1) = 0
transfer_filename(1) = transfer_out.dat
transfer_matterpower(1) = matterpower.dat
reionization = T
re_use_optical_depth = T
re_optical_depth = 0.890432E-01
re_delta_redshift = 0.5
re_ionization_frac = 1
pivot_scalar = 0.002
pivot_tensor = 0.002
initial_power_num = 1
scalar_spectral_index(1) = 0.960927E+00
scalar_nrun(1) = 0
scalar_amp(1) = 2.41147e-09
RECFAST_fudge = 1.14
RECFAST_fudge_He = 0.86
RECFAST_Heswitch = 6
initial_condition = 1
scalar_output_file = scalCls.dat
lensed_output_file = lensedCls.dat
FITS_filename = scalCls.fits
accurate_polarization = T
accurate_reionization = T
accurate_BB = T
do_late_rad_truncation = F
do_tensor_neutrinos = T
feedback_level = 1
massive_nu_approx = 3
number_of_threads = 2
accuracy_boost = 2
l_accuracy_boost = 2
l_sample_boost = 2
####################################################
And here is one sample parameter file for SYNFAST:
#####################################################
simul_type = 1
nsmax = 512
nlmax = 1024
iseed = 70
beam_file = beam_V1.fits
infile = Cls.fits
outfile = ../temp/bestfit_512_10.fits
######################################################
(the beam shouldn't matter on the large scales I analyze, but nonethelesse, I've calculated the window functions by squaring the beam transfer function, available on LAMBDA)[tex][/tex]
I'm going to analyze large-scale characteristics of the CMB and currently trying to get familiar with data evaluation and its simulation by SYNFAST.
I'm using the WMAP-5 foreground reduced temperature maps per frequency band, available on LAMBDA (e.g. wmap_band_forered_iqumap_r9_5yr_V_v3.fits). Thereby, I observed something strange when comparing the map to Gaussian simulations. To get started, I wanted to compare the rms temperature fluctuations on large scales of real maps with their simulations.
First of all, I multiply a map (e.g. the V map) by the appropriate mask for temperature analysis (wmap_ext_temperature_analysis_mask_r9_5yr_v3.fits, also on LAMBDA) and degrade it to Healpix resolution 8 (via the Healpix fortran routine UD_GRADE). Now, the map is pixelized on a scale of about 7 degrees.
Then, I calculated the map's rms temperature fluctuation on this scale, just by averaging the square of the temperature fluctuations delta_T in each pixel over the whole map and taking the square-root in the end.
This value is to be compared to Gaussian simulations, created by the Healpix facility SYNFAST based on the WMAP-5 best fit Cl spectrum (e.g. computed by CAMB with the parameter file given by LAMBDA, see below). I expected the following: Since the best-fit Cls originate from the true WMAP-maps, my real rms temperature fluctuation should be (nearly) the same as the mean value of many Gaussian simulations running through the same masking and degrading procedure.
But that is not what I observe!
The mean value of the rms temperature fluctuations of a bunch of Gaussian simulations is about a factor of 1.4 too large.
I cannot find any explanation for this discrepancy. The instrument noise can be neglected on this large scale and could only correct into the wrong direction, I guess.
Do I understand something wrong or can anybody see a mistake?
Any hints are welcome!
Cheers,
Maik
If it helps you, here is the parameter fie for CAMB as downloaded from LAMBDA:
#########################################################
output_root = bestfit
get_scalar_cls = T
get_vector_cls = F
get_tensor_cls = F
COBE_normalize = F
CMB_outputscale = 7.425625e12
get_transfer = T
do_nonlinear = 0
l_max_scalar = 2000
k_eta_max_scalar = 4000
do_lensing = T
lensing_method = 1
w = -1
cs2_lam = 1
hubble = 0.724365E+02
use_physical = T
ombh2 = 0.226823E-01
omch2 = 0.108085E+00
omnuh2 = 0
omk = 0
temp_cmb = 2.725
helium_fraction = 0.24
massless_neutrinos = 3.04
massive_neutrinos = 0
nu_mass_eigenstates = 1
nu_mass_degeneracies = 0
nu_mass_fractions = 1
transfer_high_precision = T
transfer_kmax = 2
transfer_k_per_logint = 0
transfer_num_redshifts = 1
transfer_interp_matterpower = T
transfer_power_var = 7
transfer_redshift(1) = 0
transfer_filename(1) = transfer_out.dat
transfer_matterpower(1) = matterpower.dat
reionization = T
re_use_optical_depth = T
re_optical_depth = 0.890432E-01
re_delta_redshift = 0.5
re_ionization_frac = 1
pivot_scalar = 0.002
pivot_tensor = 0.002
initial_power_num = 1
scalar_spectral_index(1) = 0.960927E+00
scalar_nrun(1) = 0
scalar_amp(1) = 2.41147e-09
RECFAST_fudge = 1.14
RECFAST_fudge_He = 0.86
RECFAST_Heswitch = 6
initial_condition = 1
scalar_output_file = scalCls.dat
lensed_output_file = lensedCls.dat
FITS_filename = scalCls.fits
accurate_polarization = T
accurate_reionization = T
accurate_BB = T
do_late_rad_truncation = F
do_tensor_neutrinos = T
feedback_level = 1
massive_nu_approx = 3
number_of_threads = 2
accuracy_boost = 2
l_accuracy_boost = 2
l_sample_boost = 2
####################################################
And here is one sample parameter file for SYNFAST:
#####################################################
simul_type = 1
nsmax = 512
nlmax = 1024
iseed = 70
beam_file = beam_V1.fits
infile = Cls.fits
outfile = ../temp/bestfit_512_10.fits
######################################################
(the beam shouldn't matter on the large scales I analyze, but nonethelesse, I've calculated the window functions by squaring the beam transfer function, available on LAMBDA)[tex][/tex]