Readme for HDLike

This is a mock CMB-HD likelihood including lensed and delensed $TT/TE/EE/BB$ CMB + lensing $\kappa\kappa$ spectra from multipoles 30 to 20,000. We also include a mock DESI BAO likelihood. The likelihood can be used with Cobaya. Please cite MacInnis, Sehgal, and Rothermel (2023).

Installation of likelihood

Installation requirements

To use the CMB-HD likelihood, you must install Python version >= 3 and NumPy.

To test the likelihood by running test_hdlike.py, you must also install CAMB.

To use the likelihood with Cobaya, you must have Python version >= 3.8 and Cobaya.

(The likelihood has been tested with Cobaya version 3.3.2 and CAMB version 1.5.0.)

Installation instructions

Simply clone this repository and install the code with pip. Navigate to the directory in which you would like to place the hdlike directory (i.e., the directory where this README.md file is located), and then run the following commands:

git clone https://github.com/CMB-HD/hdlike.git
cd hdlike
pip install . --user

To uninstall the code, use the command pip uninstall hdlike. (Note that you may have to navigate away from the hdlike directory before running this command).

Testing the likelihood

To test the likelihood, run the following command:

python test_hdlike.py

The test code above assumes the input mock CMB-HD data contains delensed CMB spectra with no baryonic feedback and no DESI BAO. You can also add a flag to test the lensed CMB spectra, spectra including baryonic feedback effects, and/or include DESI BAO by running the following commands:

python test_hdlike.py --lensed
python test_hdlike.py --feedback
python test_hdlike.py --desi

You can use more than one flag, e.g. python test_hdlike.py --desi --feedback.

This will test that the code is calculating the correct likelihood by matching the output value to a precomputed likelihood value.

Testing the likelihood with Cobaya

To test the interface between hdlike and Cobaya, run the following command:

python test_hdlike_cobaya.py

The same flag options apply as above for test_hdlike.py. This will also test that the code is outputting the correct likelihood by matching the output value to a precomputed likelihood. In addition, it will test the Cobaya initialization.

Using the likelihood with Cobaya

We provide an example file that can be input into Cobaya and run, called example_cmbhd.yaml.

To run the built-in Cobaya test of the initialization, use the following command:

cobaya-run example_cmbhd.yaml --test

Note that test_hdlike_cobaya.py loads information from the file example_cmbhd.yaml; if you’d like to modify the example file, please create your own copy.

MCMC chains can be run with the following command:

cobaya-run your_cmbhd.yaml

To run multiple chains with MPI you should follow the instructions for your machine. For NERSC, we provide a job script template, named nersc_perlmutter_job_template.sb (see the NERSC section below) that can be run from this hdlike directory with the following command:

sbatch nersc_perlmutter_job_template.sb

(You should include the name of the CMB project to which you are charging your hours somewhere in the job-name; e.g., #SBATCH --job-name=hdlike_CMBEXP. Please see below for more information about using Cobaya on NERSC.)

To analyze the MCMC chains, we provide an example Jupyter notebook named hdlike_cobaya_results.ipynb. We also provide a set of pre-computed chains that can be used to test the notebook.

Running MCMC chains on NERSC

A NERSC job script template, named nersc_perlmutter_job_template.sb, is also included.

Some NERSC/Perlmutter-specific notes:

• Save your chains in your SCRATCH directory. This will involve modifying the output path in your_cmbhd.yaml. To find your SCRATCH directory, use the command echo $SCRATCH. However, be aware that this is not permanent storage: see the NERSC documentation for more details.
• You'll need to follow the instructions in the NERSC documentation to use MPI with mpi4py.
• You must provide the name of the allocation account being used on the line #SBATCH --account= (after the = sign; e.g. #SBATCH --account=mp107 for CMB).
• You should include the name of the CMB project you are charging your hours to somewhere in the job-name (e.g., #SBATCH --job-name=hdlike_CMBEXP).
• If you're activating a conda environment within the job script, you need to use source activate instead of the conda activate used on the login nodes.

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Additional helpful information

Below we have provided some additional information (but you do not need to read further in order to use the CMB-HD likelihood as described above).

Generating a new input .yaml file for Cobaya

We also provide a script that the user can run to generate their own .yaml file to input into Cobaya. To use this script, adjust the parameters of the hdlike_settings.yaml file. (One can also adjust the settings for CAMB and Cobaya in camb_cobaya_settings.yaml, for example to change which parameters are fixed/varied, such as baryonic feedback. One can also specify a proposal matrix in this file.)

To make the new input file, run the following command:

python generate_cobaya_input_file.py hdlike_settings.yaml camb_cobaya_settings.yaml

Note on using DESI BAO with Cobaya

You may see the following error message when trying to use the provided DESI BAO likelihood with Cobaya:

cobaya.component.ComponentNotInstalledError: The data for this likelihood has not been correctly installed. To install it, run `cobaya-install bao.generic`

If this occurs, running the cobaya-install bao.generic command will clone the Cobaya bao_data repository and fix the issue. See the Cobaya docs for more information about the cobaya-install command.

Using your own Cobaya configuration file

If you have your own Cobaya configuration .yaml file already, you can use the CMB-HD likelihood following the directions below.

To add the CMB-HD likelihood

To use the CMB-HD delensed $TT/TE/EE/BB$ CMB + $\kappa\kappa$ mock spectra and covariance matrix, you only need to mention HDLike in your Cobaya configuration .yaml file or python dictionary, under the likelihood block. For example, if running Cobaya from a .yaml file, your likelihood block would be

likelihood:
	hdlike.hdlike.HDLike:

To instead use CMB-HD lensed $TT/TE/EE/BB$ CMB + $\kappa\kappa$ mock spectra and covariance matrix, set the option delensed: False under HDLike in the likelihood block:

likelihood:
	hdlike.hdlike.HDLike:
		delensed: False

Additionally, you can change the maximum multipole for the CMB or lensing mock spectra and covariance matrix by setting the option lmax or Lmax, respectively, to an integer below the default lmax = 20100 or Lmax = 20100. The minimum multipole for all spectra is fixed to 30. You can exclude the lensing power spectrum data by setting the option use_cmb_lensing_spectrum: False, or exclude the CMB $TT/TE/EE/BB$ data by setting the option use_cmb_power_spectra: False.

A basic example YAML file is provided, named example_cmbhd.yaml. The proposal widths of parameters in the parameters block were obtained from a CMB-HD Fisher matrix, located in the hdlike/data/proposal_cov/from_fisher directory. We also provide a few proposal matrices from MCMC runs in the hdlike/data/proposal_cov/from_chains directory.

• Note that you should provide your own output path at the top of the file; the chain files that are output may be large. See the Cobaya docs for details.

To add the DESI BAO likelihood

To combine CMB-HD with mock DESI BAO data, simply add the following lines to your likelihood block:

likelihood:
	hdlike.hdlike.HDLike:
		delensed: True
	bao.generic:
		measurements_file: /PATH/TO/hdlike/hdlike/data/mock_desi_bao_rs_over_DV_data.txt
		cov_file: /PATH/TO/hdlike/hdlike/data/mock_desi_bao_rs_over_DV_cov.txt

The example YAML file example_cmbhd.yaml contains a sample bao.generic block, but you must replace /PATH/TO/ with the absolute path to your hdlike directory (the directory in which this README is located). See the Cobaya docs for more information about the generic BAO likelihood.