The LIghtweight Source finding Algorithms (LiSA) library finds HI sources in next generation radio surveys. LiSA can analyze input data cubes of any size with pipelines that automatically decompose data into different domains for parallel distributed analysis. For source finding, the library contains python modules for wavelet denoising of 3D spatial and spectral data, and robust automatic source finding using null-hypothesis testing. The source-finding algorithms all have options to automatically choose parameters, minimizing the need for manual fine tuning. Finally, LiSA also contains neural network architectures for classification and characterization of 3D spectral data.

Herculens models imaging data of strong gravitational lenses. The package supports various degrees of model complexity, ranging from standard smooth analytical profiles to pixelated models and machine learning approaches. In particular, it implements multiscale pixelated models regularized with sparsity constraints and wavelet decomposition, for modeling both the source light distribution and the lens potential. The code is fully differentiable - based on JAX (ascl:2111.002) - which enables fast convergence to the solution, access to the parameters covariance matrix, efficient exploration of the parameter space including the sampling of posterior distributions using variational inference or Hamiltonian Monte-Carlo methods.

mgcnn is a Convolutional Neural Network (CNN) architecture for classifying standard and modified gravity (MG) cosmological models based on the weak-lensing convergence maps they produce. It is implemented in Keras using TensorFlow as the backend. The code offers three options for the noise flag, which correspond to noise standard deviations, and additional options for the number of training iterations and epochs. Confusion matrices and evaluation metrics (loss function and validation accuracy) are saved as numpy arrays in the generated output/ directory after each iteration.