PlanetFlow is an automated, all-in-one post-processing pipeline designed for high-resolution planetary astrophotography. It streamlines the traditional, multi-software workflow into a single execution. The pipeline integrates automated stacking, wavelet sharpening, 3D sphere projection for planetary de-rotation, RGB channel alignment, and sequential GIF generation. By automating notoriously tedious steps like manual de-rotation coordinate matching, PlanetFlow significantly reduces processing time for large batches of planetary video data. The tool is available as standalone executables for both Windows and Linux environments, catering to remote observatory and mini-PC setups.

SED_Tools is a Python package for downloading, standardizing, combining, and preparing stellar atmosphere spectral energy distribution libraries and photometric filter profiles for astrophysical synthetic photometry workflows. It provides unified access to multiple stellar atmosphere data sources, converts spectra and filters to consistent units and formats, builds lookup tables and flux cubes, merges separate atmosphere grids into combined libraries, and generates outputs compatible with MESA's colors module. The package includes both command-line and Python interfaces, along with tools for rebuilding and packaging spectral libraries. It also includes machine-learning components for extending incomplete SEDs and generating SEDs from stellar parameters. SED_Tools is intended to support reproducible stellar photometry, atmosphere-grid preparation, and model-observation comparison in computational astrophysics.

Abstract: dvsg calculates the kinematic disturbance parameter ΔV⋆−g (pronounced 'DVSG'), which quantifies the offset between the stellar and gas velocity maps of a galaxy obtained via integral field spectroscopy (IFS). The motivation behind ΔV⋆−g is to capture disturbances in the kinematics of a galaxy that might be missed by alternative methods, while also attempting to minimize bias towards any galaxy properties or features of the IFS data. The code provides lightweight functions to compute ΔV⋆−g, visualise results, and work with data from the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey on which it was developed.

ClearSkyRFI predicts radio frequency interference from satellite constellations during radio astronomy observations. Given an observatory location, observation target, and time window, the tool identifies interference-free observation windows using a two-stage pipeline combining SOPP coarse visibility screening with a gain-based beam filter. Both Airy disk and manual top-hat beam models are supported. Results are exportable as timestamped CSV files and animated sky plots. A PyQt6 graphical interface is provided, with pre-built installers available for Linux, Windows, and macOS.

Starithm is a real-time platform that aggregates and structures multi-messenger astronomy alerts from NASA's General Coordinates Network (GCN). It ingests both machine-readable GCN Kafka streaming notices and human-authored GCN Circulars, unifying them into a single structured view per canonical transient event. An AI pipeline extracts measurements and entities from Circular free text using fine-tuned LLMs, enabling structured querying across historically unstructured data. It also has the capability to summarising the entire event history. As of early 2026, the platform has processed over 9,000 GCN Circulars and 2,100 streaming notices spanning 739 canonical events, covering gravitational wave candidates, gamma-ray bursts, neutrino events, and other high-energy transients. Starithm is accessible via a web interface at https://starithm.ai.

This pipeline forecasts constraints on the local primordial non-Gaussianity parameter fNL using the joint angular power spectra of Fast Radio Burst (FRB) Dispersion Measure (DM) and timing delay fields. The key result is that the DM × timing cross-spectrum self-calibrates the electron bias parameter b_e, dramatically improving fNL constraints.

FARGOpy is a Python package designed for the analysis and visualization of hydrodynamical simulations produced by the FARGO3D code. It provides a reproducible and physically consistent analysis pipeline for refined spherical grids, where curvilinear geometry and spatial non-uniformity often hinder the use of generic tools. The package supports the manipulation of scalar and vector fields, offering tools for multidimensional interpolation, flux calculation through arbitrary surfaces, and kinematic analysis in planetocentric frames. It is particularly robust for post-processing planet-disk interaction simulations and studying circumplanetary environments.

MASSKIP reduces, calibrates, and performs photometric analysis on images acquired with Multi-Amplifier Sensing (MAS) Skipper CCDs. It handles amplifier heterogeneity and deep sub-electron read noise regimes. The Python pipeline supports Multi-Extension FITS (MEF) files, executing overscan correction per extension with independent ROI shifting, pixel-by-pixel bias subtraction, and normalized master flat creation. It applies variance-weighted and signal-to-noise ratio-weighted combination methods to dynamically suppress noisy amplifiers and achieve theoretical sub-electron read noise limits. MASSKIP also includes enhanced cosmic ray rejection, dynamic WCS injection, and a built-in photometry module utilizing photutils.

GpuFitsCrypt integrates a flexible policy engine for fine-grained access control with a GPU-accelerated implementation of the AES-GCM authenticated encryption protocol for massive astronomical catalogs in FITS format. The framework implements a novel parallel tree-reduction strategy to overcome the inherently sequential Galois/Counter Mode (GCM) authentication hash (GHASH) bottleneck, achieving authenticated encryption throughput exceeding 380 MB/s suitable for petabyte-scale astronomical data. GpuFitsCrypt provides a robust mechanism for data providers to enforce access policies ensuring both confidentiality and integrity without hindering research workflows.

growpacity computes mean opacities for dust populations with arbitrary composition, maximum grain size a_max, and size distribution power-law index q. It uses optool (ascl:2104.010) to compute frequency-dependent absorption and scattering coefficients over a configurable parameter grid, which are averaged into Rosseland and Planck mean opacities as functions of a_max, q, and temperature. The results are stored in lightweight tables that can be efficiently interpolated via provided C and Python interfaces, enabling use in high-performance radiation hydrodynamics and dust evolution simulations with dynamically varying grain size distributions.