https://github.com/planck-npipe/lollipop
A cobaya low-ell likelihood polarized for planck 2020 data (NPIPE release)
https://github.com/planck-npipe/lollipop
cmb cobaya likelihood planck
Last synced: about 2 months ago
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A cobaya low-ell likelihood polarized for planck 2020 data (NPIPE release)
- Host: GitHub
- URL: https://github.com/planck-npipe/lollipop
- Owner: planck-npipe
- License: gpl-3.0
- Created: 2020-11-25T13:42:53.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2026-03-03T06:10:59.000Z (3 months ago)
- Last Synced: 2026-03-03T09:59:18.086Z (3 months ago)
- Topics: cmb, cobaya, likelihood, planck
- Language: Python
- Homepage:
- Size: 111 KB
- Stars: 7
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
LoLLiPoP: Low-L Likelihood Polarized for Planck
================================================
[](https://github.com/planck-npipe/lollipop/actions/workflows/testing.yml)
[](https://pypi.python.org/pypi/planck-2020-lollipop)
[](https://www.gnu.org/licenses/gpl-3.0)
``Lollipop`` is a Planck low-l polarization likelihood based on cross-power-spectra for which the
bias is zero when the noise is uncorrelated between maps. It uses the approximation presented in
[Hamimeche & Lewis (2008)](https://arxiv.org/abs/0801.0554), modified as described in [Mangilli et
al. (2015)](https://arxiv.org/abs/1503.01347) to apply to cross-power spectra. This version is
based on the Planck PR4 data. Cross-spectra are computed on the CMB maps from Commander component
separation applied on each detset-split Planck frequency maps.
It was previously applied and described in
- [Planck Collaboration Int. XLVII (2016)](https://arxiv.org/abs/1605.03507) for investigating the
reionization history,
- [Tristram et al. (2021)](https://arxiv.org/abs/2010.01139) Planck constraints on the tensor-to-scalar ratio
- [Tristram et al. (2022)](https://arxiv.org/abs/2112.07961) Improved limits on the tensor-to-scalar ratio using BICEP and Planck data
It is interfaced with the ``cobaya`` MCMC sampler.
Requirements
------------
* Python >= 3.5
* `numpy`
* `astropy`
Install
-------
The easiest way to install the `Lollipop` likelihood is *via* `pip`
```shell
pip install planck-2020-lollipop [--user]
```
If you plan to dig into the code, it is better to clone this repository to some location
```shell
git clone https://github.com/planck-npipe/lollipop.git /where/to/clone
```
Then you can install the `Lollipop` likelihoods and its dependencies *via*
```shell
pip install -e /where/to/clone
```
The ``-e`` option allow the developer to make changes within the `Lollipop` 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 Lollipop likelihood data
-----------------------------------
You should use the `cobaya-install` binary to automatically download the data needed by the
`lollipop.lowlE` or `lollipop.lowlB` or `lollipop.lowlEB` likelihoods
```shell
cobaya-install /where/to/clone/examples/test_lollipop.yaml -p /where/to/put/packages
```
Data and code such as [CAMB](https://github.com/cmbant/CAMB) will be downloaded and installed within
the ``/where/to/put/packages`` directory. For more details, you can have a look to `cobaya`
[documentation](https://cobaya.readthedocs.io/en/latest/installation_cosmo.html).
Likelihood versions
-------------------
* ``lowlE``
* ``lowlB``
* ``lowlEB``