HiLLiPoP: High-L Likelihood Polarized for Planck

Hillipop is a multifrequency CMB likelihood for Planck data. The likelihood is a spectrum-based Gaussian approximation for cross-correlation spectra from Planck 100, 143 and 217GHz split-frequency maps, with semi-analytic estimates of the Cl covariance matrix based on the data. The cross-spectra are debiased from the effects of the mask and the beam leakage using Xpol (a generalization to polarization of the algorithm presented in Tristram et al. 2005) before being compared to the model, which includes CMB and foreground residuals. They cover the multipoles from ℓ=30 to ℓ=2500.

The model consists of a linear combination of the CMB power spectrum and several foregrounds residuals. These are:

• Galactic dust (estimated directly from the 353 GHz channel);
• the cosmic infrared background (as measured in Planck Collaboration XXX 2014);
• thermal Sunyaev-Zeldovich emission (based on the Planck measurement reported in Planck Collaboration XXI 2014);
• kinetic Sunyaev-Zeldovich emission, including homogeneous and patchy reionization components from Shaw et al. (2012) and Battaglia et al. (2013);
• a tSZ-CIB correlation consistent with both models above; and
• unresolved point sources as a Poisson-like power spectrum.

HiLLiPoP has been used as an alternative to the public Planck likelihood in the 2013 and 2015 Planck releases [Planck Collaboration XV 2014; Planck Collaboration XI 2016]. Its last version v4.2, based on Planck PR4, is described in detail in Tristram et al. (2023).

Likelihoods available are

• hillipop.TT, hillipop.EE, hillipop.TE, and hillipop.TTTEEE (ℓ by ℓ)
• hillipop.TT_bin, hillipop.TTTEEE_bin (binned version with δℓ=10 after 250)

It is interfaced with the cobaya MCMC sampler.

References

When used, please cite the following article:

Cosmological parameters derived from the final Planck data release (PR4)
M. Tristram, A.J. Banday, M. Douspis, X. Garrido, K.M. Górski, S. Henrot-Versillé, L.T. Hergt, S. Ilić, R. Keskitalo, G. Lagache, C.R. Lawrence, B. Partridge, D. Scott
A&A, (2023)
DOI: https://doi.org/10.1051/0004-6361/202348015

Likelihood versions

• Planck 2020 (v4.1.0, PR4)
• Planck 2020 (v4.2.2, PR4)
• Planck 2020 (v4.3, PR4 including binned version)

Install

The easiest way to install the Hillipop likelihood is via pip

pip install planck-2020-hillipop [--user]

If you plan to dig into the code, it is better to clone this repository to some location

git clone https://github.com/planck-npipe/hillipop.git /where/to/clone

Then you can install the Hillipop likelihood and its dependencies via

pip install -e /where/to/clone

The -e option allow the developer to make changes within the Hillipop directory without having to reinstall at every changes. If you plan to just use the likelihood and do not develop it, you can remove the -e option.

Installing Hillipop likelihood data

The examples/hillipop_example.yaml file is a good starting point to know the different nuisance parameters used by hillipop likelihoods.

You should use the cobaya-install binary to automatically download the data needed by the Hillipop likelihood

cobaya-install /where/to/clone/examples/hillipop_example.yaml -p /where/to/put/packages

Data and code such as CAMB will be downloaded and installed within the /where/to/put/packages directory. For more details, you can have a look to cobaya documentation.

Chains

We provide here results from MCMC exploration chains, as well as yaml files used for Cobaya runs and covariance matrices corresponding to the results presented in M. Tristram, et al. A&A (2023).

Requirements

• Python >= 3.5
• numpy
• astropy