Basic cosmology runs¶

Sampling from a cosmological posterior works exactly as the examples at the beginning of the documentation, except one usually needs to add a theory code, and possibly some of the cosmological likelihoods presented later.

You can sample or track any parameter that is understood by the theory code in use (or any dynamical redefinition of those). You do not need to modify Cobaya’s source to use new parameters that you have created by modifying CLASS or modifying CAMB, or to create a new cosmological likelihood and track its parameters.

Creating from scratch the input for a realistic cosmological case is quite a bit of work. But to make it simpler, we have created an automatic input generator, that you can run from the shell as:

$cobaya-cosmo-generator  Note If PySide is not installed, this will fail. To fix it: $ pip3 install pyside2


$conda install -c conda-forge pyside2  Warning In Python 2 (soon to be discontinued!) try one of the following: $ sudo apt install python-pyside

$pip install PySide2 # add --user if it fails  Start by choosing a preset, maybe modify some aspects using the options provided, and copy or save the generated input to a file, either in yaml form or as a python dictionary. The parameter combinations and options included in the input generator are in general well-tested, but they are only suggestions: you can add by hand any parameter that your theory code or likelihood can understand, or modify any setting. Don’t forget to add your installation path for the cosmological modules as modules: '/path/to/modules', and an output prefix if you wish. As an example, here is the input for Planck 2015 base $$\Lambda\mathrm{CDM}$$, both for CLASS and CAMB: Click to toggle CAMB/CLASS CAMB parameter names: likelihood: planck_2018_lowl.TT: null planck_2018_lowl.EE: null planck_2018_highl_plik.TTTEEE: null planck_2018_lensing.clik: null params: logA: prior: min: 1.61 max: 3.91 ref: dist: norm loc: 3.05 scale: 0.001 proposal: 0.001 latex: \log(10^{10} A_\mathrm{s}) drop: true As: value: 'lambda logA: 1e-10*np.exp(logA)' latex: A_\mathrm{s} ns: prior: min: 0.8 max: 1.2 ref: dist: norm loc: 0.965 scale: 0.004 proposal: 0.002 latex: n_\mathrm{s} theta_MC_100: prior: min: 0.5 max: 10 ref: dist: norm loc: 1.04109 scale: 0.0004 proposal: 0.0002 latex: 100\theta_\mathrm{MC} drop: true renames: theta cosmomc_theta: value: 'lambda theta_MC_100: 1.e-2*theta_MC_100' derived: false H0: latex: H_0 min: 20 max: 100 ombh2: prior: min: 0.005 max: 0.1 ref: dist: norm loc: 0.0224 scale: 0.0001 proposal: 0.0001 latex: \Omega_\mathrm{b} h^2 omch2: prior: min: 0.001 max: 0.99 ref: dist: norm loc: 0.12 scale: 0.001 proposal: 0.0005 latex: \Omega_\mathrm{c} h^2 omegam: latex: \Omega_\mathrm{m} omegamh2: derived: 'lambda omegam, H0: omegam*(H0/100)**2' latex: \Omega_\mathrm{m} h^2 mnu: 0.06 omega_de: latex: \Omega_\Lambda YHe: latex: Y_\mathrm{P} Y_p: latex: Y_P^\mathrm{BBN} DHBBN: derived: 'lambda DH: 10**5*DH' latex: 10^5 \mathrm{D}/\mathrm{H} tau: prior: min: 0.01 max: 0.8 ref: dist: norm loc: 0.055 scale: 0.006 proposal: 0.003 latex: \tau_\mathrm{reio} zre: latex: z_\mathrm{re} sigma8: latex: \sigma_8 s8h5: derived: 'lambda sigma8, H0: sigma8*(H0*1e-2)**(-0.5)' latex: \sigma_8/h^{0.5} s8omegamp5: derived: 'lambda sigma8, omegam: sigma8*omegam**0.5' latex: \sigma_8 \Omega_\mathrm{m}^{0.5} s8omegamp25: derived: 'lambda sigma8, omegam: sigma8*omegam**0.25' latex: \sigma_8 \Omega_\mathrm{m}^{0.25} A: derived: 'lambda As: 1e9*As' latex: 10^9 A_\mathrm{s} clamp: derived: 'lambda As, tau: 1e9*As*np.exp(-2*tau)' latex: 10^9 A_\mathrm{s} e^{-2\tau} age: latex: '{\rm{Age}}/\mathrm{Gyr}' rdrag: latex: r_\mathrm{drag} chi2__CMB: derived: 'lambda chi2__planck_2018_lowl_TT, chi2__planck_2018_lowl_EE, chi2__planck_2018_highl_plik_TTTEEE, chi2__planck_2018_lensing_clik: sum([chi2__planck_2018_lowl_TT, chi2__planck_2018_lowl_EE, chi2__planck_2018_highl_plik_TTTEEE, chi2__planck_2018_lensing_clik])' latex: \chi^2_\mathrm{CMB} sampler: mcmc: covmat: auto drag: true proposal_scale: 1.9 theory: camb: extra_args: halofit_version: mead bbn_predictor: PArthENoPE_880.2_standard.dat lens_potential_accuracy: 1 num_massive_neutrinos: 1 nnu: 3.046 theta_H0_range: - 20 - 100  CLASS parameter names: likelihood: planck_2018_lowl.TT: null planck_2018_lowl.EE: null planck_2018_highl_plik.TTTEEE: null planck_2018_lensing.clik: null params: logA: prior: min: 1.61 max: 3.91 ref: dist: norm loc: 3.05 scale: 0.001 proposal: 0.001 latex: \log(10^{10} A_\mathrm{s}) drop: true A_s: value: 'lambda logA: 1e-10*np.exp(logA)' latex: A_\mathrm{s} n_s: prior: min: 0.8 max: 1.2 ref: dist: norm loc: 0.965 scale: 0.004 proposal: 0.002 latex: n_\mathrm{s} theta_s_1e2: prior: min: 0.5 max: 10 ref: dist: norm loc: 1.0416 scale: 0.0004 proposal: 0.0002 latex: 100\theta_\mathrm{s} drop: true 100*theta_s: value: 'lambda theta_s_1e2: theta_s_1e2' derived: false H0: latex: H_0 omega_b: prior: min: 0.005 max: 0.1 ref: dist: norm loc: 0.0224 scale: 0.0001 proposal: 0.0001 latex: \Omega_\mathrm{b} h^2 omega_cdm: prior: min: 0.001 max: 0.99 ref: dist: norm loc: 0.12 scale: 0.001 proposal: 0.0005 latex: \Omega_\mathrm{c} h^2 Omega_m: latex: \Omega_\mathrm{m} omegamh2: derived: 'lambda Omega_m, H0: Omega_m*(H0/100)**2' latex: \Omega_\mathrm{m} h^2 m_ncdm: value: 0.06 renames: mnu Omega_Lambda: latex: \Omega_\Lambda YHe: latex: Y_\mathrm{P} tau_reio: prior: min: 0.01 max: 0.8 ref: dist: norm loc: 0.055 scale: 0.006 proposal: 0.003 latex: \tau_\mathrm{reio} z_reio: latex: z_\mathrm{re} sigma8: latex: \sigma_8 s8h5: derived: 'lambda sigma8, H0: sigma8*(H0*1e-2)**(-0.5)' latex: \sigma_8/h^{0.5} s8omegamp5: derived: 'lambda sigma8, Omega_m: sigma8*Omega_m**0.5' latex: \sigma_8 \Omega_\mathrm{m}^{0.5} s8omegamp25: derived: 'lambda sigma8, Omega_m: sigma8*Omega_m**0.25' latex: \sigma_8 \Omega_\mathrm{m}^{0.25} A: derived: 'lambda A_s: 1e9*A_s' latex: 10^9 A_\mathrm{s} clamp: derived: 'lambda A_s, tau_reio: 1e9*A_s*np.exp(-2*tau_reio)' latex: 10^9 A_\mathrm{s} e^{-2\tau} age: latex: '{\rm{Age}}/\mathrm{Gyr}' rs_drag: latex: r_\mathrm{drag} chi2__CMB: derived: 'lambda chi2__planck_2018_lowl_TT, chi2__planck_2018_lowl_EE, chi2__planck_2018_highl_plik_TTTEEE, chi2__planck_2018_lensing_clik: sum([chi2__planck_2018_lowl_TT, chi2__planck_2018_lowl_EE, chi2__planck_2018_highl_plik_TTTEEE, chi2__planck_2018_lensing_clik])' latex: \chi^2_\mathrm{CMB} sampler: mcmc: covmat: auto drag: true proposal_scale: 1.9 theory: classy: extra_args: non linear: halofit N_ncdm: 1 N_ur: 2.0328  Note Note that Planck likelihood parameters (or nuisance parameters) do not appear in the input: they are included automatically at run time. The same goes for all internal likelihoods (i.e. those listed below in the table of contents). You can still add them to the input, if you want to redefine any of their properties (its prior, label, etc.). See Changing and redefining parameters; inheritance. Save the input generated to a file and run it with cobaya-run [your_input_file_name.yaml]. This will create output files as explained here, and, after some time, you should be able to run GetDistGUI to generate some plots. Typical running times for MCMC when using computationally heavy likelihoods (e.g. those involving $$C_\ell$$, or non-linear $$P(k,z)$$ for several redshifts) are ~10 hours running 4 MPI processes with 4 OpenMP threads per process, provided that the initial covariance matrix is a good approximation to the one of the real posterior (Cobaya tries to select it automatically from a database; check the [mcmc] output towards the top to see if it succeded), or a few hours on top of that if the initial covariance matrix is not a good approximation. It is much harder to provide typical PolyChord running times. We recommend starting with a low number of live points and a low convergence tolerance, and build up from there towards PolyChord’s default settings (or higher, if needed). Post-processing cosmological samples¶ Let’s suppose that we want to importance-reweight a Plank sample, in particular the one we just generated with the input above, with some late time LSS data from BAO. To do that, we add the new BAO likelihoods. We would also like to increase the theory code’s precision with some extra arguments: we will need to re-add it, and set the new precision parameter under extra_args (the old extra_args will be inherited, unless specifically redefined). Since we do not need to recompute the CMB likelihoods, which are not too affected by the new precision parameter. On top of that, let us add a derived parameter. Assuming we saved the sammple at chains/planck, we need to define the following input file, which we can run with $ cobaya-run:

# Path the original sample
ouput: chains/planck

# Post-processing information
post:
suffix: BAO  # the new sample will be called "chains\planck_post_des*"
# If we want to skip the first third and take 1 every 3 samples
skip: 0.3
thin: 3
# Now let's add the DES likelihood,
# increase the precision (remember to repeat the extra_args)
# and add the new derived parameter
likelihood:
sixdf_2011_bao:
sdss_dr7_mgs:
sdss_dr12_consensus_bao:
theory:
# Use *only* the theory corresponding to the original sample
classy:
extra_args:
# New precision parameter
# [option]: [value]
camb:
extra_args:
# New precision parameter
# [option]: [value]
params:
# h = H0/100. (nothing to add: CLASS/CAMB knows it)
h:
# A dynamic derived parameter: sum of BAO chi-squared's
chi2__BAO:
derived: 'lambda chi2__sixdf_2011_bao, chi2__sdss_dr7_mgs, chi2__sdss_dr12_consensus_bao:
sum([chi2__sixdf_2011_bao, chi2__sdss_dr7_mgs, chi2__sdss_dr12_consensus_bao])'
latex: \chi^2_\mathrm{BAO}


Warning

In the current implementation, likelihood recomputation does not automatically trigger recomputation of the partial “chi2” sums as the one in the basic Planck examples above, chi2__cmb. If you are recomputing one likelihood that is part of a partial sum, you need to re-define them inside the add block.

Getting help and bibliography for a module¶

If you want to get the available options with their default values for a given module, use

$cobaya-doc [module_name]  If the module name is not unique (i.e. there are more than one module with the same name but different kinds), use the option --kind [module_kind] to specify its kind: sampler, theory or likelihood. Call $ cobaya-doc with a kind instead of a module name (e.g. $cobaya-doc likelihood) to get a list of modules of that kind. Call with no arguments to get all available modules of all kinds. If you would like to cite the results of a run in a paper, you would need citations for all the different parts of the process. In the example above that would be this very sampling framework, the MCMC sampler, the CAMB or CLASS cosmological code and the Planck 2018 likelihoods. The bibtex for those citations, along with a short text snippet for each element, can be easily obtained and saved to some output_file.tex with $ cobaya-bib [your_input_file_name.yaml] > output_file.tex


You can pass multiple input files this way, or even a (list of) module name(s), as in cobaya-doc.

You can also do this interactively, by passing your input info, as a python dictionary, to the function citation():

from cobaya.bib import get_bib_info
get_bib_info(info)


Note

Both defaults and bibliography are available in the GUI (menu Show defaults and bilbiography for a module ...).

Bibliography for preset input files is displayed in the bibliography tab.