BornRaytrace uses neural data compression of weak lensing map summary statistics to simulate weak gravitational lensing effects. It can raytrace through overdensity Healpix maps to return a convergence map, include shear-kappa transformation on the full sphere, and also include intrinsic alignments (NLA model).

CosmoPower develops Bayesian inference pipelines that leverage machine learning to solve inverse problems in science. While the emphasis is on building algorithms to accelerate Bayesian inference in cosmology, the implemented methods allow for their application across a wide range of scientific fields. CosmoPower provides neural network emulators of matter and Cosmic Microwave Background power spectra, which can replace Boltzmann codes such as CAMB (ascl:1102.026) or CLASS (ascl:1106.020) in cosmological inference pipelines, to source the power spectra needed for two-point statistics analyses. This provides orders-of-magnitude acceleration to the inference pipeline and integrates naturally with efficient techniques for sampling very high-dimensional parameter spaces.

provabgs infers full posterior distributions of galaxy properties for galaxies in the DESI Bright Galaxy Survey using state-of-the-art Bayesian spectral energy distribution (SED) modeling of DESI spectroscopy and photometry. provabgs includes a state-of-the-art stellar population synthesis (SPS) model based on non-parametric prescription for star formation history, a metallicity history that varies over the age of the galaxy, and a flexible dust prescription. It has a neural network emulator for the SPS model that enables accelerated inference. Full posteriors of the 12 SPS parameters can be derived in ~10 minutes. The emulator is currently designed for galaxies from 0 < z < 0.6. provabgs also includes a Bayesian inference pipeline that is based on zeus (ascl:2008.010).