TABASCAL (TrAjectory BAsed RFI Subtraction and CALibration) models and subtracts radio-frequency interference (RFI) in post-correlation radio-interferometry data by jointly fitting trajectories of RFI sources and antenna gain solutions. It builds a source-to-visibility forward model that incorporates near-field moving RFI (such as satellites or aircraft) and uses JAX (ascl:2111.002) and Dask to scale across CPUs and GPUs for calibration and subtraction tasks. The toolkit accepts visibility data, metadata on RFI trajectories or two-line elements, and YAML-based simulation configuration files to generate cleaned visibilities and produce diagnostics of gain and RFI fit quality. TABASCAL enables calibration workflows that recover astronomical signals with reduced data loss by explicitly modeling RFI trajectories rather than simply flagging contaminated data.

Astronomaly actively detects anomalies in astronomical data. A python back-end runs anomaly detection based on machine learning; a JavaScript front-end provides data viewing and labeling. The package works on many common astronomy data types, including one-dimensional data and images, and offering extendable techniques for preprocessing, feature extraction, and machine learning.

The Fisher4Cast suite, which requires MatLab, provides a standard, tested tool set for general Fisher Information matrix prediction and forecasting for use in both research and education. The toolbox design is robust and modular, allowing for easy additions and adaptation while keeping the user interface intuitive and easy to use. Fisher4Cast is completely general but the default is coded for cosmology. It provides parameter error forecasts for cosmological surveys providing distance, Hubble expansion and growth measurements in a general, curved FLRW background.