PyBird

The Python code for Biased tracers in redshift space

• EFT predictions for correlators of biased tracers in redshift space
• Likelihoods of galaxy-clustering data with EFT predictions

General info

Fast correlator computation

• One-loop EFT predictions for two-point (2pt) functions:
  - dark matter or biased tracers
  - real or redshift space
  - Fourier (power spectrum) or configuration space (correlation function)
• Additional modeling:
  - geometrical (AP) distortion
  - survey mask
  - binning
  - exact-time dependence
  - wedges / P-statistics
  - and more...

Likelihoods with EFT predictions

Currently available:

BOSS DR12 LRG 2pt full-shape + rec. bao
eBOSS DR16 QSO 2pt full-shape

Soon available:

[BOSS DR12 LRG 3pt full-shape]

Dependencies

PyBird depends on the numerical libraries NumPy and SciPy.

The following packages are not strictly neccessary but recommended to run the cookbooks:

• PyBird has extra compatibility with CLASS.
• PyBird likelihoods are integrated within MontePython 3.
• PyBird likelihoods are showcased with iminuit and emcee.

Installation

Clone the repo, and install it as a Python package using pip:

git clone https://github.com/pierrexyz/pybird.git
cd pybird
pip install .

That's it, now you can simply import pybird from wherever in your projects.

Getting Started -- likelihood

If you are a MontePython 3 user, likelihoods can be installed 'with less than a cup of coffee'.

• Clone and install PyBird as above
• Copy the likelihood folder montepython/likelihoods/eftboss to your working MontePython repository: montepython_public/montepython/likelihoods/
• Copy the data folder data/eftboss to your working MontePython data folder: montepython_public/data/
• Try to run the likelihood of BOSS DR12 with the input param file montepython/eftboss.param

*** Note (23/03/08): MontePython v3.5 seems to have some incompatibilities with the PyBird likelihood related to the function data.need_cosmo_arguments(). To resolve it, see this pull-request.

That's it, you are all set!

• If any doubt, benchmark $\Lambda$CDM posteriors are shown here.
• Posterior covariances for Metropolis-Hasting Gaussian proposal (in MontePython format) can be found here.

Cookbooks

Alternatively, if you are curious, here are three cookbooks that should answer the following questions:

• Correlator: How to ask PyBird to compute EFT predictions?
• Likelihood: How does the PyBird likelihood work?
• Data: What are the data read by PyBird likelihood?
• cbird: What is the algebra of the EFT predictions PyBird is based on?

Documentation

Read the docs at https://pybird.readthedocs.io.

Attribution

• Written by Pierre Zhang and Guido D'Amico
• License: MIT
• Special thanks to: Arnaud de Mattia, Thomas Colas, Théo Simon, Luis Ureña

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

G. D’Amico, L. Senatore and P. Zhang, "Limits on wCDM from the EFTofLSS with the PyBird code", JCAP 01 (2021) 006, 2003.07956

The BibTeX entry for it is:

@article{DAmico:2020kxu,
    author = "D'Amico, Guido and Senatore, Leonardo and Zhang, Pierre",
    title = "{Limits on $w$CDM from the EFTofLSS with the PyBird code}",
    eprint = "2003.07956",
    archivePrefix = "arXiv",
    primaryClass = "astro-ph.CO",
    doi = "10.1088/1475-7516/2021/01/006",
    journal = "JCAP",
    volume = "01",
    pages = "006",
    year = "2021"
}

We would be grateful if you also cite the theory papers when relevant:

The Effective-Field Theory of Large-Scale Structure: 1004.2488, 1206.2926

One-loop power spectrum of biased tracers in redshift space: 1610.09321

Exact-time dependence: 2005.04805, 2111.05739

Wedges / P-statistics: 2110.00016

When using the likelihoods, here are some relevant references:

BOSS DR12 data: 1607.03155, catalogs: 1509.06529, patchy mocks (for covariance estimation): 1509.06400

BOSS DR12 LRG power spectrum measurements: from 2206.08327, using Rustico

BOSS DR12 LRG correlation function measurements: from 2110.07539, using FCFC

BOSS DR12 LRG rec. bao parameters: from 2003.07956, based on post-reconstructed measurements from 1509.06373

BOSS DR12 survey mask measurements: following 1810.05051 with integral constraints and consistent normalization following 1904.08851, from fkpwin, using nbodykit

BOSS EFT likelihood: besides the PyBird paper, see also: 1909.05271, 1909.07951, 2110.07539

eBOSS DR16 data: 2007.08991, catalogs: 2007.09000, EZmocks (for covariance estimation): 1409.1124

eBOSS DR16 QSO power spectrum + survey mask measurements: from 2106.06324

eBOSS EFT likelihood: 2210.14931

*** Disclaimer: due to updates in the data and the prior definition, it is possible that results obtained with up-to-date likelihoods differ slightly with the ones presented in the articles.