Im3shape forward-fits a galaxy model to each data image it is supplied with and reports the parameters of the best fitting model, including the ellipticity components. It uses the Discrete Fourier Transform (DFT) to render images of convolved galaxy profiles, calculates the maximum likelihood parameter values, and corrects for noise bias. IM3SHAPE is a modular C code with a significant amount of Python glue code to enable setting up new models and their options automatically.

DeepSphere implements a generalization of Convolutional Neural Networks (CNNs) to the sphere. It models the discretized sphere as a graph of connected pixels. The resulting convolution is more efficient (especially when data doesn't span the whole sphere) and mostly equivariant to rotation (small distortions are due to the non-existence of a regular sampling of the sphere). The pooling strategy exploits a hierarchical pixelization of the sphere (HEALPix) to analyze the data at multiple scales. The graph neural network model is based on ChebNet and its TensorFlow implementation.

UFalcon rapidly post-processes N-body code output into signal maps for many different cosmological probes. The package is able to produce maps of weak-lensing convergence, linear-bias galaxy over-density, cosmic microwave background (CMB) lensing convergence and the integrated Sachs-Wolfe temperature perturbation given a set of N-body lightcones. It offers high flexibility for lightcone construction, such as user-specific survey-redshift ranges, redshift distributions and single-source redshifts. UFalcon also computes the galaxy intrinsic alignment signal, which can be treated as an additive component to the cosmological signal.