I'm trying to use CosmoMC with background parameters only, but I can't get it to work. I've followed the tutorials and guides I can find online for doing this but I keep getting the following errror:
skipped unused params: omegabh2 omegach2 theta tau logA ns calPlanck acib217 xi asz143 aps100 aps143 aps143217 aps217 aksz kgal100 kgal143 kgal143217 kgal217 cal0 cal2
computing r>0 but compute_tensors=F
I don't think the first 2 lines are an error but I show them anyway because they support my suspicion that CosmoMC is using params_CMB.paramnames instead of params_background.paramnames.
In order to try to get CosmoMC to use the background parameterization I have made the following changes to version vMay2020-3:
1) In cosmomc/source/driver.F90 I change
call Setup%Config%SetTheoryParameterization(Ini, baseParams%NameMapping, 'theta')
call Setup%Config%SetTheoryParameterization(Ini, baseParams%NameMapping, 'background')
2) In the subroutine BK_ParamArrayToTheoryParams(this, Params, CMB) in cosmomc/source/CosmologyParameterization.f90 I changed CMB%omb=omegam-CMB%omnu to CMB%omb=0.04 and CMB%omc=0 to CMB%omc = omegam - CMB%omb-CMB%omnu.
After "make clean; make" I run by typing ./cosmomc test.ini so I modify test.ini to read:
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DEFAULT(batch3/MyH.ini) parameterization = background #e.g. to vary r in addition to standard 6: #(for r>0 also need compute_tensors=T) #compute_tensors = T #param[r] = 0.03 0 2 0.04 0.04 #high for new runs, to start learning new proposal matrix quickly MPI_Max_R_ProposeUpdate = 30 propose_matrix= planck_covmats/base_TT_lensing_lowE_lowl_plikHM.covmat #Folder where files (chains, checkpoints, etc.) are stored root_dir = chains/ #Root name for files produced file_root=test #action= 0 runs chains, 1 importance samples, 2 minimizes #use action=4 just to quickly test likelihoods action = 4 #expected result for -(log like) test_check_compare = 1184.788 #Don't need to set this if OMP_NUM_THREADS set appropriately num_threads = 0 #if you want to get theory cl for test point #test_output_root = output_cl_root start_at_bestfit =F feedback=1 use_fast_slow = T #turn on checkpoint for real runs where you want to be able to continue them checkpoint = F #sampling_method=7 is a new fast-slow scheme good for Planck sampling_method = 7 dragging_steps = 3 propose_scale = 2 #Set >0 to make data files for importance sampling indep_sample=10 get_sigma8=F
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batch_name = batch3 parameterization = background use_SN = T redo_no_new_data=T redo_add=T redo_likeoffset=0 prior_name = base_plikHM_TTTEEE_lowE param[omegam] = 0.31 0 1 0.01 0.005 param[H0] = 70 20 100 0.1 0.1 param[omegak] = 0 param[mnu] = 0.06 param[w]= -1 param[wa] = 0 param[nnu] = 3.046 highL_theory_cl_template = %DATASETDIR%HighL_lensedCls.dat bbn_consistency=F #Feedback level ( 2=lots,1=chatty,0=none) feedback = 2 #If zero set automatically num_threads = 0 #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 = 0.01 #1: Simple Metropolis, 2: slice sampling, 3: slice sampling fast parameters, 4: directional gridding #7 is new dragging method sampling_method = 7 dragging_steps = 3 use_fast_slow = T ##Rest are fairly irrelevant #if sampling_method =4, iterations per gridded direction directional_grid_steps = 20 #action = 0: MCMC, action=1: postprocess .data file, action=2: find best fit point only action = 0
Does anyone have any idea what I'm missing in order to be able to do a background run using only supernova data?