PyFowl

The Python code for Fast Observables in Weak Lensing

• EFT one-loop predictions for 2D angular projected two-point functions:
  • cosmic shear
  • galaxy-galaxy lensing
  • galaxy clustering
• Dark Energy Survey Y3 3x2pt likelihood with EFT predictions

Installation

git clone https://github.com/pierrexyz/pyfowl.git
cd pyfowl
pip install -e .

Running with MontePython

To run with MontePython 3, once PyFowl is installed as above,

• Copy the likelihood folder montepython/likelihoods/eftdes to your working MontePython repository: montepython_public/montepython/likelihoods/

• Copy the data folder data/eftdes to your working MontePython data folder: montepython_public/data/

• Run the DES-Y3 3x2pt EFT likelihood with the input param file montepython/input/maglim_noz56.param

• Posterior covariances for Metropolis-Hasting Gaussian proposal (in MontePython format) can be found here.

Attribution

• Devs:
  • Pierre Zhang
  • Guido D'Amico
• License: MIT

When using PyFowl in a publication, please acknowledge the code by citing the following paper:

G. D’Amico, A. Refregier, L. Senatore, and P. Zhang, "The cosmological analysis of DES 3$\times$2pt data from the Effective Field Theory of Large-Scale Structure", 2510.24878

The BibTeX entry is:

@article{DAmico:2025zui,
    author = "D'Amico, Guido and Refregier, Alexandre and Senatore, Leonardo and Zhang, Pierre",
    title = "{The cosmological analysis of DES 3$\times$2pt data from the Effective Field Theory of Large-Scale Structure}",
    eprint = "2510.24878",
    archivePrefix = "arXiv",
    primaryClass = "astro-ph.CO",
    month = "10",
    year = "2025"
}

When using the Dark Energy Survey Year 3 data products in a publication, please refer to https://des.ncsa.illinois.edu/releases/y3a2 for acknowledgements.