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ABCMB

Python+JAX package for differentiable CMB+BBN computations. See https://abcmb.readthedocs.io/en/latest/ for documentation.

Install / Use

/learn @TonyZhou729/ABCMB
About this skill

Quality Score

0/100

Supported Platforms

Universal

README

<h1 align="center"> ABCMB<!-- omit from toc --> </h1> <h4 align="center">

License: MIT arXiv Run Tests

<!--[![arXiv](https://img.shields.io/badge/arXiv-2408.14538%20-green.svg)](https://arxiv.org/abs/2408.14538) --> </h4>

Autodifferentiable Boltzmann solver for the CMB (ABCMB) is a Python+JAX package for differentiable computation of the Cosmic Microwave Background. ABCMB is complete to linear order in $\Lambda\rm{CDM}$ cosmology. It computes the matter and CMB power spectra and includes effects like lensing, massive neutrinos, and a state-of-the-art treatment of the physics of recombination through the companion code HyRex.

Installation

ABCMB is pip installable! Just run

pip install ABCMB

We recommend always doing so in a conda environment, preferably even a clean one.

If you'd like to clone the repo instead, after cloning you can run

pip install .

from the code directory.

Note that both methods of installing will automatically attempt to install JAX for CPU; to install for GPU, refer to the JAX documentation for a quick JAX installation guide.

Examples

We have included several pedagogical jupyter notebooks to walk you through how to get started with ABCMB in our example_notebooks folder. We suggest you start with ABCMB_basics to get a sense of how to run the code. If you'd like to add new physics to ABCMB, check out ABCMB_Fluids. If you'd like to run ABCMB with the Big Bang Nucleosynthesis (BBN) code LINX to do BBN+CMB joint analyses, check out ABCMB_with_LINX.

Issues

Please feel free to open an issue if something is amiss in ABCMB!

Citation

If you use ABCMB to publish scientific research, we suggest you cite

@misc{abcmb,
      title={{ABCMB: A Python+JAX Package for the Cosmic Microwave Background Power Spectrum}}, 
      author={Zilu Zhou and Cara Giovanetti and Hongwan Liu},
      year={2026},
      eprint={2602.15104},
      archivePrefix={arXiv},
      primaryClass={astro-ph.CO},
      url={https://arxiv.org/abs/2602.15104}, 
}
View on GitHub
GitHub Stars15
CategoryDevelopment
Updated1d ago
Forks0

Languages

Jupyter Notebook

Security Score

90/100

Audited on Apr 8, 2026

No findings