SAQQARA analyzes stochastic gravitational wave background signals. This Simulation-based Inference (SBI) library is built on top of the swyft code (ascl:2302.016), which implements neural ratio estimation to efficiently access marginal posteriors for all parameters of interest. Simulation-based inference combined with implicit marginalization (over nuisance parameters) has been shown to be well suited for SGWB data analysis.

PEREGRINE performs full parameter estimation on gravitational wave signals. Using an internal Truncated Marginal Neural Ratio Estimation (TMNRE) algorithm and building upon the swyft (ascl:2302.016) code to efficiently access marginal posteriors, PEREGRINE conducts a sequential simulation-based inference approach to support the analysis of both transient and continuous gravitational wave sources. The code can fully reconstruct the posterior distributions for all parameters of spinning, precessing compact binary mergers using waveform approximants.

Albatross analyzes Milky Way stellar streams. This Simulation-Based Inference (SBI) library is built on top of swyft (ascl:2302.016), which implements neural ratio estimation to efficiently access marginal posteriors for all parameters of interest. Using swyft for its internal Truncated Marginal Neural Ratio Estimation (TMNRE) algorithm and sstrax (ascl:2306.008) for fast simulation and modeling, Albatross provides a modular inference pipeline to support parameter inference on all relevant parts of stellar stream models.

sstrax provides fast simulations of Milky Way stellar stream formation. Using JAX (ascl:2111.002) acceleration to support code compilation, sstrax forward models all aspects of stream formation, including evolution in gravitational potentials, tidal disruption and observational models, in a fully modular way. Although sstrax is a standalone python package, it was also developed to integrate directly with the Albatross (ascl:2306.009) inference pipeline, which performs inference on all relevant aspects of the stream model.