candl-like 1.0.0

Differentiable Likelihood for CMB Analysis

CMB Analysis With A Differentiable Likelihood

Authors:

L. Balkenhol, C. Trendafilova, K. Benabed, S. Galli

Paper:

arxiv shield goes here

Source:

https://github.com/Lbalkenhol/candl

Documentation:

right here

candl is a differentiable likelihood framework for analysing CMB power spectrum measurements. Key features are:

• JAX-compatibility, allowing for fast and easy computation of gradients and Hessians of the likelihoods.

• The latest public data releases from the South Pole Telescope and Atacama Cosmology Telescope collaborations.

• Interface tools for work with other popular cosmology software packages (e.g. Cobaya and MontePython).

• Auxiliary tools for common analysis tasks (e.g. generation of mock data).

candl supports the analysis of primary CMB and lensing power spectrum data (\(TT\), \(TE\), \(EE\), \(BB\), \(\phi\phi\), \(\kappa\kappa\)).

Installation

candl can be installed with pip:

pip install candl-like

After installation, we recommend testing by executing the following python code:

import candl.tests
candl.tests.run_all_tests()

This well test all data sets included in candl.

JAX

JAX is a Google-developed python library. In its own words: “JAX is Autograd and XLA, brought together for high-performance numerical computing.”

candl is written in a JAX-friendly way. That means JAX is optional and you can install and run candl without JAX and perform traditional inference tasks such as MCMC sampling with Cobaya. However, if JAX is installed, the likelihood is fully differentiable thanks to automatic differentiation and many functions are jitted for speed.

Documentation

You can build the documentation by executing:

make html

in the docs/ folder. There may be some warnings, but there should be no errors. You can then access the documentation under docs/_build/html/index.html. Once the repository is public, we can host this properly somewhere.

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