HIDE (HI Data Emulator) forward-models the process of collecting astronomical radio signals in a single dish radio telescope instrument and outputs pixel-level time-ordered-data. Written in Python, HIDE models the noise and RFI modeling of the data and with its companion code SEEK (ascl:1607.020) provides end-to-end simulation and processing of radio survey data.

SEEK (Signal Extraction and Emission Kartographer) processes time-ordered-data from single dish radio telescopes or from the simulation pipline HIDE (ascl:1607.019), removes artifacts from Radio Frequency Interference (RFI), automatically applies flux calibration, and recovers the astronomical radio signal. With its companion code HIDE (ascl:1607.019), it provides end-to-end simulation and processing of radio survey data.

tf_unet mitigates radio frequency interference (RFI) signals in radio data using a special type of Convolutional Neural Network, the U-Net, that enables the classification of clean signal and RFI signatures in 2D time-ordered data acquired from a radio telescope. The code is not tied to a specific segmentation and can be used, for example, to detect radio frequency interference (RFI) in radio astronomy or galaxies and stars in widefield imaging data. This U-Net implementation can outperform classical RFI mitigation algorithms.

KaRMMa (Kappa Reconstruction for Mass MApping) performs curved-sky mass map reconstruction using a lognormal prior from weak-lensing surveys. It uses a fully Bayesian approach with a physically motivated lognormal prior to sample from the posterior distribution of convergence maps. The posterior distribution of KaRMMa maps are nearly unbiased in one-point and two-point functions and peak/void counts. KaRMMa successfully captures the non-Gaussian nature of the distribution of κ values in the simulated maps, and KaRMMa posteriors correctly characterize the uncertainty in summary statistics.