Minimally Informed CMB MAp Constructor: MICMAC

[Release coming soon]

MICMAC Logo, credits: Ema Tsang King Sang

Pixel implementation of the non-parametric component separation. Extension to component separation method of Leloup et al. (2023)

Note: Make sure not to use this package on supercomputer LOGIN NODES or it will use all the available resources.

Installation

The easiest way to install and use micmac is the following

clone the repo

git clone https://github.com/CMBSciPol/MICMAC && cd MICMAC

create a virtual environment with the required dependencies
```
conda env create -f micmac_env.yml
```

install micmac

conda activate micmac_env
python -m pip install .

Note: this package uses the JAX library, for which you should follow the official installation guide in order to make sure the installation can go through.

How to use `micmac`

You will find in the tutorials directory a list of notebooks showcasing how to use micmac.

The tutorials make use of an additional Python library not installed by default

cmbdb

python -m pip install git+https://github.com/dpole/cmbdb

Note: If you fork the repository and want to commit some changes, you may want to use pre-commit. When committing with pre-commit, your changes will probably be reformatted, you must then re-add them and re-commit.

License

This code is released under the GPLv3 license, which can be found in the LICENSE file.

Contact

For any solicitation, please contact morshed at apc.in2p3.fr or rizzieri at apc.in2p3.fr

Citation

If you use micmac, please consider citing:

@misc{morshed2024pixel,
      title={Pixel domain implementation of the Minimally Informed CMB MAp foreground Cleaning (MICMAC) method},
      author={Magdy Morshed and Arianna Rizzieri and Cl\'ement Leloup and Josquin Errard and Radek Stompor},
      year={2024},
      eprint={2405.18365},
      archivePrefix={arXiv},
      primaryClass={astro-ph.CO}
}
@article{Leloup:2023vkb,
    author = "Leloup, Cl\'ement and Errard, Josquin and Stompor, Radek",
    title = "{Nonparametric maximum likelihood component separation for CMB polarization data}",
    eprint = "2308.03412",
    archivePrefix = "arXiv",
    primaryClass = "astro-ph.CO",
    doi = "10.1103/PhysRevD.108.123547",
    journal = "Phys. Rev. D",
    volume = "108",
    number = "12",
    pages = "123547",
    year = "2023"
}

Magwos/MICMAC_old_version