https://github.com/cmbant/planckearlylcdm
Planck parameter chains independent of late-time cosmology
https://github.com/cmbant/planckearlylcdm
Last synced: about 2 months ago
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Planck parameter chains independent of late-time cosmology
- Host: GitHub
- URL: https://github.com/cmbant/planckearlylcdm
- Owner: cmbant
- Created: 2024-12-15T20:11:23.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2024-12-18T14:59:04.000Z (5 months ago)
- Last Synced: 2025-02-13T21:48:00.793Z (3 months ago)
- Size: 9.01 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Planck PR4 TTTEEE+lowE+lensing Early ΛCDM Parameter Chains
This repository contains the early ΛCDM cosmological parameter chains derived from the Planck from the paper **"CMB Constraints on the Early Universe Independent of Late-Time Cosmology"** by Pablo Lemos and Antony Lewis ([arXiv:2302.12911](https://arxiv.org/abs/2302.12911)).
## Overview
The early ΛCDM parameter chains allow for robust constraints on the early universe's physics while being minimally influenced by assumptions about late-time cosmology. By leveraging empirical constraints on CMB lensing and weak priors on integrated effects such as the Sachs-Wolfe effect and foreground contributions, these chains provide insights into the early universe that are independent of the complexities of late-time structure growth.
## Data
The chains were generated using [Cobaya](https://github.com/CobayaSampler/cobaya) and use:
- Planck PR4 CamSpec likelihood
- Temperature and polarization data (TTTEEE) at ℓ ≥ 30
- Low-ℓ EE polarization data
- Planck PR4 lensing likelihood
## Methodology
Parameters are constrained using an approach that:
- Models CMB lensing empirically using a spline fit to the lensing power spectrum
- Excludes low-ℓ temperature data (ℓ < 30) to avoid ISW sensitivity
- Models residual ISW at ℓ ≥ 30 with a template
- Uses empirical foreground templates
- Treats reionization through a single τ parameter
## Usage
Chains can be analysed or visualized using [GetDist](https://getdist.readthedocs.io/).
The .covmat file has the parameter covariance for Gaussian approximations.
To use a simple Gaussian approximation to the likelihood in Cobaya you can use [gaussian_mixture](https://cobaya.readthedocs.io/en/latest/likelihood_gaussian_mixture.html), e.g. for 3-parameters
```
likelihood:
gaussian_mixture:
means: [[1.04103e-2, 0.02223, 0.1192]]
covs: [[6.8552146e-16, 1.4486860e-12, -1.4105674e-11],
[1.4486860e-12, 2.1344167e-08, -1.1534501e-07],
[-1.4105674e-11, -1.1534501e-07, 1.6977630e-06]]
input_params: ['thetastar', 'ombh2', 'omch2']
output_params: []
```
Note that you must have thetastar, ombh2 and omch2 defined in the same order in your input sampling yaml.
## Citation
If you use these chains or the associated analyses in your work, please cite:
```
@article{Lemos:2023xhs,
author = "Lemos, Pablo and Lewis, Antony",
title = "{CMB constraints on the early Universe independent of late-time cosmology}",
eprint = "2302.12911",
archivePrefix = "arXiv",
primaryClass = "astro-ph.CO",
doi = "10.1103/PhysRevD.107.103505",
journal = "Phys. Rev. D",
volume = "107",
number = "10",
pages = "103505",
year = "2023"
}ar={2023}
}
```