HelioPy provides a set of tools to download and read in data, and carry out other common data processing tasks for heliospheric and planetary physics. It handles a wide variety of solar and satellite data and builds upon the SpiceyPy package (ascl:1903.016) to provide an accessible interface for performing orbital calculations. It has also implemented a framework to perform transformations between some common coordinate systems.

Molsoft operates, monitors and schedules observations, both through predetermined schedule files and fully dynamically, at the refurbished Molonglo Observatory Synthesis Radio Telescope (MOST). It was developed as part of the UTMOST upgrade of the facility. The software runs a large-scale pulsar timing program; the autonomous observing system and the dynamic scheduler have increased the observing efficiency by a factor of 2-3 in comparison with static scheduling.

prodimopy is an open-source Python package to read, analyze and plot modelling results of the radiation thermo-chemical disk code ProDiMo (PROtoplanetary DIsk MOdel, https://prodimo.iwf.oeaw.ac.at). It also includes tools to run ProDiMo in 1D slap model mode, to run simple ProDimo model grids and to interface ProDiMo with 1D and 2D disk codes (i.e. use input structure from hydrodynamic models).

prodimopy can also be used independently of ProDiMo (no ProDiMo installation is required) and hence is also useful to extract information from already available ProDiMo models (e.g. as input for other codes) or for model comparison.

SZiFi (pronounced "sci-fi") implements the iterative multi-frequency matched filter (iMMF) galaxy cluster finding method. It can be used to detect galaxy clusters with mm intensity maps through their thermal Sunyaev-Zeldovich (tSZ) signal. As a novel feature, SZiFi can perform foreground deprojection via a spectrally constrained MMF or sciMMF, and can also be used for point source detection.

We develop a Physics-Informed Neural Network (PINN) code to solve the modified Teukolsky equation under realistic astrophysical conditions. The code embeds domain-specific physics—spin-weighted curvature perturbations, quasi-normal mode (QNM) boundary conditions, and attenuation dynamics—directly into the training loss function. Applied to data from the GW190521 event, the model accurately infers complex QNM frequencies (ω = 0.2917 − 0.0389i) and learns an attenuation coefficient α = 0.04096, corresponding to a 14.4% decay rate. The code demonstrates strong predictive performance, reducing mean squared error by 50.3% (MSE = 0.2537 vs. 0.5310) compared to Bayesian baselines, and achieving a positive R² score. It further reveals non-trivial r–t coupling and gravitational memory effects, which standard exponential decay models fail to capture. This PINN-based implementation establishes a computationally efficient and accurate tool for environmental modeling in gravitational wave astrophysics and offers a path forward for black hole spectroscopy beyond vacuum assumptions.