lilit 1.4.2

Likelihood for LiteBIRD

LiLit: Likelihood for LiteBIRD

Author: Giacomo Galloni

LiLit (Likelihood for LiteBIRD) is a framework for forecasting likelihoods for the LiteBIRD CMB polarization satellite. It provides a common framework for LiteBIRD researchers working within the Cobaya cosmological analysis ecosystem.

Quick Start

Install LiLit from PyPI:

pip install lilit

Basic usage:

from lilit import LiLit

# Create a likelihood for temperature and polarization
fields = ["t", "e", "b"]
likelihood = LiLit(
    fields=fields,
    lmax=[1500, 1200, 900],
    lmin=[20, 2, 2], 
    fsky=[1.0, 0.8, 0.6]
)

Key Features

• Multiple field support: Temperature, E-mode, B-mode polarization, and lensing
• Flexible configuration: Field-specific multipole ranges and sky fractions
• Multiple likelihood approximations: Exact, Gaussian, and correlated Gaussian
• Seamless Cobaya integration: Drop-in replacement for existing likelihood codes
• Extensible design: Easy integration of custom noise models and fiducial spectra

Documentation

📖 Complete documentation is available at https://lilit.readthedocs.io/

The documentation includes:

• Installation guide and quick start tutorial
• Detailed examples for common LiteBIRD use cases
• Theoretical background on likelihood approximations
• API reference with full class and function documentation
• Cobaya integration guide for parameter estimation and model comparison

Examples

See the examples directory and the online documentation for working examples including:

• Basic temperature and polarization analysis
• Multi-field likelihood configurations
• Integration with Cobaya sampling chains
• Custom noise model implementations

Contributing

Contributions are welcome! Please see our documentation for development guidelines, or open an issue to discuss major changes.

Citation

If you use LiLit in your research, please cite this repository and the relevant cosmological codes. Use Cobaya's cobaya-bib script to generate appropriate citations for your specific analysis.

Support

• Documentation: https://lilit.readthedocs.io/
• Issues: GitHub Issues
• Cobaya documentation: https://cobaya.readthedocs.io/