S2LET provides efficient routines for fast wavelet analysis of signals on the sphere. It supports both axisymmetric and directional wavelet transforms. The wavelet transforms are theoretically exact, i.e. the original signal can be synthesised from its wavelet coefficients exactly since the wavelet coefficients capture all the information content of band-limited signals.
It is primarily intended to work with the SSHT code built on our novel sampling theorem on the sphere to perform exact spherical harmonic transforms on the sphere.
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.
snmachine is a flexible python library for reading in photometric supernova light curves, extracting useful features from them and subsequently performing supervised machine learning to classify supernovae based on their light curves. The library is also flexible enough to easily extend to general transient classification.
SSHT provides functionality to perform fast and exact spin spherical harmonic transforms based on the sampling theorem on the sphere derived in our paper: A novel sampling theorem on the sphere.
In some applications adjoint forward and inverse spherical harmonic transforms are also required (for example, when solving convex optimisation problems). Functionality to perform fast and exact adjoint transforms is also included, based on the fast algorithms derived in our paper: Sparse image reconstruction on the sphere: implications of a new sampling theorem.