S2SCAT is a Python package for computing scattering covariances on the sphere using JAX. It exploits autodiff to provide differentiable transforms, which are also deployable on hardware accelerators (e.g. GPUs and TPUs), leveraging the differentiable and accelerated spherical harmonic and wavelet transforms implemented in S2FFT and S2WAV, respectively. Scattering covariances are useful both for field-level generative modelling of complex non-Gaussian textures and for statistical compression of high dimensional field-level data, a key step of e.
S2WAV is a JAX package for computing wavelet transforms on the sphere and rotation group. It leverages autodiff to provide differentiable transforms, which are also deployable on modern hardware accelerators (e.g. GPUs and TPUs), and can be mapped across multiple accelerators.
More specifically, S2WAV provides support for scale-discretised wavelet transforms on the sphere and rotation group (for both real and complex signals), with support for adjoints where needed, and comes with a variety of different optimisations (e.
SILC provides functionality to perform a novel internal linear combination (ILC) algorithm for foreground separation using directional, scale-discretised wavelets –- Scale-discretised, directional wavelet ILC or SILC.
We provide new foreground cleaned maps of the CMB temperature and polarisation anisotropies (as measured by Planck) reconstructed with SILC, which are available here.
SILC relies on the S2LET code to compute fast wavelet transforms of signals on the sphere, and the SSHT and SO3 codes to compute fast harmonic transforms on the sphere and rotation group, respectively.