PyBird Documentation

PyBird: Python for Biased Tracers in Redshift Space

PyBird is a Python package for computing power spectra and correlation functions for biased tracers in redshift space using the Effective Field Theory of Large Scale Structure (EFTofLSS).

What is PyBird?

PyBird provides fast and accurate predictions for:

• One-loop EFT predictions for two-point functions of dark matter or biased tracers

• Real and redshift space calculations

• Fourier space (power spectrum) and configuration space (correlation function) outputs

• JAX acceleration for high-performance computing with model-independent neural-network emulators (no pretraining)

• Additional modeling including geometrical distortion, survey effects, and exact-time dependence

Getting Started

New to PyBird? Start with the Correlator Tutorial notebook which showcases the basic usage and features of PyBird.

Demo Notebooks

• Demo Notebooks

The demo notebooks provide hands-on tutorials for all PyBird capabilities. They are the best way to learn PyBird!

Quick Setup

Setup Guide

• Installation Guide

API Reference

API Documentation

• pybird
  • pybird package

Key Features

• Fast correlator computation with optimized algorithms

• Neural network emulator for 1000x speedup in parameter inference (with no pretraining)

• JAX acceleration with JIT compilation, vectorization, and automatic differentiation

• MontePython integration for cosmological parameter inference

• BOSS and eBOSS likelihoods for real data analysis

• Comprehensive API for custom analyses

Developers

• Pierre Zhang [main author]

• Guido D’Amico [main author]

• Alexander Reeves

Additional Resources

• GitHub Repository

• PyBird arXiv Paper

• PyBird-JAX arXiv Paper

• MontePython Integration

Indices and tables

• Index

• Module Index

• Search Page

Special Thanks For Contributions

• Marco Bonici

• Thomas Colas

• Arnaud de Mattia

• Yan Lai

• Zhiyu Lu

• Théo Simon

• Luis Ureña

• Henry Zheng