LIGHT implements a Bayesian framework for reconstructing the three-dimensional galaxy and underlying dark matter fields informed by large-scale structure. The software uses Hamiltonian Monte Carlo (HMC) sampling via NumPyro (ascl:2505.005) to infer the true galaxy density distribution and its magnitude distribution given incomplete galaxy catalog data, addressing biases in statistical analyses that combine galaxy catalogs with other observations. LIGHT’s models incorporate spatial correlations of galaxies on cosmological scales and enable construction of improved three-dimensional priors in redshift and sky position for host galaxies of gravitational-wave events. LIGHT supports dark siren cosmology, where gravitational-wave distances are combined with reconstructed galaxy fields to improve inference of cosmological parameters, and includes example configurations and scripts.

The statistical software package pySELFI implements the Simulator Expansion for Likelihood-Free Inference (SELFI) algorithm. SELFI is part of the family of simulation-based inference (SBI) methods, which replace the use of the likelihood function with a data-generating "black-box" simulator. pySELFI supports different black-box simulators, optimizes prior hyperparameters for primordial power spectrum inference, and check for model misspecification using the Mahalanobis distance. The code also scores compression and calculation of Fisher-Rao distances.