S2FIL provides functionality to support optimal filtering on the sphere. Optimal directional matched (MF) and scale adaptive (SAF) filters may be constructed from a template and stochastic background process. Functionality is also incorporated to filter a sky data map with an optimal filter. Moreover, a simple object detection algorithm is implemented to extract embedded object parameters from the filtered field.
Functionality to support functions defined on the sphere is provided by the S2 code; template objects are defined in the COMB code; and functionality to perform fast directional filtering on the sphere is provided by the FastCSWT code.
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.
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.
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.
stringgen is a tool for creating emulating cosmic string maps. It uses wavelet phase harmonics to calculate scattering coefficients of the simulations. These coefficients can then be used to synthesize new cosmic string realisations that have similar statistics to the original simulations.