We set num_cls to 4 in cmbtypes.f90. Our settings in params.ini are as follows:
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#Sample parameters for cosmomc in default parameterization
#Root name for files produced
file_root = chains/test
#action = 0: MCMC, action=1: postprocess .data file, action=2: find best fit point only
action = 0
#Maximum number of chain steps
samples = 200000
#Feedback level ( 2=lots,1=chatty,0=none)
feedback = 1
#Temperature at which to Monte-Carlo
temperature = 1
#filenames for CMB datasets and SZ templates (added to Cl times parameter(13))
#Note you may need to change lmax in cmbtypes.f90 to use small scales (e.g. lmax=2100)
cmb_numdatasets = 1
cmb_dataset1 = WMAP
cmb_dataset_SZ1 = data/WMAP_SZ_VBand.dat
cmb_dataset_SZ_scale1 = 1
cmb_dataset2 = data/acbar2007_v3_corr.newdat
cmb_dataset_SZ2 = data/WMAP_SZ_VBand.dat
cmb_dataset_SZ_scale2 = 0.28
cmb_dataset3 = data/CBIpol_2.0_final.newdat
cmb_dataset4 = data/B03_NA_21July05.newdat
#filenames for matter power spectrum datasets, incl twodf
mpk_numdatasets = 1
mpk_dataset1 = data/lrgDR7kmax02kmin02newmaxLv2ALL_MAGCOVv3.dataset
#mpk_dataset1 = data/sdss_lrgDR4.dataset
#mpk_dataset1 = data/2df_2005.dataset
#filename for supernovae (default SDSS compilation)
SN_filename = data/supernovae.dataset
#if true, use HALOFIT for non-linear corrections (astro-ph/0207664).
#note lyman-alpha (lya) code assumes linear spectrum
nonlinear_pk = F
use_CMB = T
use_HST = T
use_mpk = F
use_BAO = T
use_clusters = F
use_BBN = F
use_Age_Tophat_Prior = F
use_SN = F
use_lya = F
use_min_zre = 0
#directory, e.g. window functions in directory windows under data_dir
data_dir = data/
#Force computation of sigma_8 even if use_mpk = F
get_sigma8 = F
#1: Simple Metropolis, 2: slice sampling, 3: slice sampling fast parameters, 4: directional gridding
sampling_method = 1
#if sampling_method =4, iterations per gridded direction
directional_grid_steps = 20
#use fast-slow parameter distinctions to speed up
#(note for basic models WMAP3 code is only ~3x as fast as CAMB)
use_fast_slow = F
#Can use covariance matrix for proposal density, otherwise use settings below
#Covariance matrix can be produced using "getdist" program.
propose_matrix = params_CMB_pivot002.covmat
#If propose_matrix is blank (first run), can try to use numerical Hessian to
#estimate a good propose matrix. As a byproduct you also get an approx best fit point
estimate_propose_matrix = F
#Tolerance on log likelihood to use when estimating best fit point
delta_loglike = 2
#Scale of proposal relative to covariance; 2.4 is recommended by astro-ph/0405462 for Gaussians
#If propose_matrix is much broader than the new distribution, make proportionately smaller
#Generally make smaller if your acceptance rate is too low
propose_scale = 2.4
#Increase to oversample fast parameters more, e.g. if space is odd shape
oversample_fast = 1
#if non-zero number of steps between sample info dumped to file file_root.data
indep_sample = 0
#number of samples to disgard at start; usually set to zero and remove later
burn_in = 0
#If zero set automatically
num_threads = 2
#MPI mode multi-chain options (recommended)
#MPI_Converge_Stop is a (variance of chain means)/(mean of variances) parameter that can be used to stop the chains
#Set to a negative number not to use this feature. Does not guarantee good accuracy of confidence limits.
MPI_Converge_Stop = 0.03
#Do initial period of slice sampling; may be good idea if
#cov matrix or widths are likely to be very poor estimates
MPI_StartSliceSampling = F
#Can optionally also check for convergence of confidence limits (after MPI_Converge_Stop reached)
#Can be good idea as small value of MPI_Converge_Stop does not (necessarily) imply good exploration of tails
MPI_Check_Limit_Converge = F
#if MPI_Check_Limit_Converge = T, give tail fraction to check (checks both tails):
MPI_Limit_Converge = 0.025
#permitted quantile chain variance in units of the standard deviation (small values v slow):
MPI_Limit_Converge_Err = 0.2
#which parameter's tails to check. If zero, check all parameters:
MPI_Limit_Param = 0
#if MPI_LearnPropose = T, the proposal density is continally updated from the covariance of samples so far (since burn in)
MPI_LearnPropose = T
#can set a value of converge at which to stop updating covariance (so that it becomes rigorously Markovian)
#e.g. MPI_R_StopProposeUpdate = 0.4 will stop updating when (variance of chain means)/(mean of variances) < 0.4
MPI_R_StopProposeUpdate = 0.4
#If have covmat, R to reach before updating proposal density (increase if covmat likely to be poor)
#Only used if not varying new parameters that are fixed in covmat
MPI_Max_R_ProposeUpdate = 2
#As above, but used if varying new parameters that were fixed in covmat
MPI_Max_R_ProposeUpdateNew = 30
#if blank this is set from system clock
rand_seed =
#If true, generate checkpoint files and terminated runs can be restarted using exactly the same command
#and chains continued from where they stopped
#With checkpoint=T note you must delete all chains/file_root.* files if you want new chains with an old file_root
checkpoint = F
#CAMB parameters
#If we are including tensors
compute_tensors = T
#Initial power spectrum amplitude point (Mpc−1)
pivot_k = 0.002
#If using tensors, enforce nT = -AT/(8As)
inflation_consistency = T
#Whether the CMB should be lensed (slows a lot unless also computing matter power)
CMB_lensing = T
#increase accuracy_level to run CAMB on higher accuracy
#(default is about 0.3%, accuracy_level=2 around 0.1% at high l)
accuracy_level = 1
#If action = 1
redo_likelihoods = T
redo_theory = F
redo_cls = F
redo_pk = F
redo_skip = 0
redo_outroot =
redo_thin = 1
redo_add = F
redo_from_text = F
#If large difference in log likelihoods may need to offset to give sensible weights
#for exp(difference in likelihoods)
redo_likeoffset = 0
#parameter start center, min, max, start width, st. dev. estimate
param[omegabh2] = 0.0223 0.005 0.1 0.001 0.001
param[omegadmh2] = 0.105 0.01 0.99 0.01 0.01
param[theta] = 1.04 0.5 10 0.002 0.002
param[tau] = 0.09 0.01 0.8 0.03 0.03
param[omegak] = 0 0 0 0 0
param[fnu] = 0 0 0 0 0
param[w] = −1 −1 −1 0 0
param[ns] = 0.95 0.5 1.5 0.02 0.01
param[nt] = 0 0 0 0 0
param[nrun] = 0 0 0 0 0
#log[1010 As]
param[logA] = 3 2.7 4 0.01 0.01
param[r] = 0 0 2 0.01 0.01
#SZ amplitude, as in WMAP analysis
param[asz]= 1 0 2 0.4 0.4
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Others are the default settings. What's wrong with my running? Would you tell me how to set parameters and options to reproduce the results of the WMAP team?
Moreover, how can we get the smooth contours like the WMAP team's? Increase the number of chains or decrease the value of MPI_Converge_Stop?
Thanks.
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