tropygal is a pure-python package for entropy estimates in the context of galactic dynamics, but can be used to estimate the entropy in any context. It currently focuses on nearest-neighbor estimators, and it also provides a function for density estimates. Additionally, it provides functions for analytical distribution functions and density of states for dynamical models that have analytical expressions.

We present AstroContour, an open-source Python-based image processing tool designed for the detection, extraction, and analysis of contours in astronomical imagery. Developed using the OpenCV computer vision library, the code applies a sequence of pre-processing steps—such as grayscale conversion, noise reduction, thresholding, and morphological operations—followed by contour detection and refinement techniques. This approach is optimized for identifying the outlines of celestial objects, including lunar crescents, planetary disks, and other extended sources, under varying atmospheric and imaging conditions. The framework is adaptable to both raw and pre-processed astrophotographic data, enabling researchers to isolate features of interest, measure geometric parameters, and prepare datasets for further scientific analysis. AstroContour provides a reproducible, modular, and extensible workflow for astronomers, educators, and citizen science projects engaged in observational astronomy.

halox is a JAX-powered Python library for differentiable dark matter halo property and mass function calculations.

While significant advances have been made in photometric classification ahead of the millions of transient events and hundreds of supernovae (SNe) each night that the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will discover, classifying SNe spectroscopically remains the best way to determine most subtypes of SNe. Traditional spectrum classification tools use template matching techniques (Blondin & Tonry 2007) and require significant human supervision. Two deep learning spectral classifiers, DASH (Muthukrishna et al. 2019) and SNIascore (Fremling et al. 2021) define the state of the art, but SNIascore is a binary classifier devoted to maximizing the purity of the SN Ia-norm sample, while DASH is no longer maintained and the original work suffers from contamination of multi-epoch spectra in the training and test sets. We have explored several neural network architectures in order to create a new automated method for classifying SN subtypes, settling on an attention-based model we call ABC-SN. We benchmark our results against an updated version of DASH, thus providing the community with an up-to-date general purpose SN classifier. Our dataset includes ten different SN subtypes including subtypes of SN Ia, core collapse and interacting SNe. We find that ABC-SN outperforms DASH, and we discuss the possibility that modern SN spectra datasets contain label noise which limit the performance of all classifiers.

We have developed a software pipeline, AutoWISP, for extracting high-precision photometry from citizen scientists' observations made with consumer-grade color digital cameras (digital single-lens reflex, or DSLR, cameras), based on our previously developed tool, AstroWISP. The new pipeline is designed to convert these observations, including color images, into high-precision light curves of stars. We outline the individual steps of the pipeline and present a case study using a Sony-alpha 7R II DSLR camera, demonstrating sub-percent photometric precision, and highlighting the benefits of three-color photometry of stars. Project PANOPTES will adopt this photometric pipeline and, we hope, be used by citizen scientists worldwide. Our aim is for AutoWISP to pave the way for potentially transformative contributions from citizen scientists with access to observing equipment.

We present AstroWISP: a collection of image processing tools for source extraction, background determination, point spread function/pixel response function fitting, and aperture photometry. AstroWISP is particularly well-suited for working with detectors featuring a Bayer mask (an array of microfilters applied to each detector pixel to allow color photography), such as consumer DSLR cameras. Such detectors pose significant challenges for existing tools while offering a much cheaper alternative to specialized devices. As a result, consumer DSLR cameras with Bayer masks are often underutilized for precision photometry. AstroWISP addresses this limitation in an effort to democratize precision photometry and support broader community participation in research. We demonstrate that our tools produce high-precision photometry from such images, enabling the use of such devices for detecting exoplanet transits. We package our tools for all major operating systems to ensure accessibility for amateur astronomers.

FRion computes and applies corrections for the effects of Faraday rotation by the Earth's ionosphere in radio polarization data. Other ionospheric Faraday rotation codes can compute the predicted Faraday rotation measure as a function of time and direction, and in some cases apply the correction directly to visibilities. FRion has been made to tackle the case where direct correction in visibilities is not possible and correction must occur post-imaging. FRion computes the time-averaged Faraday rotation across the duration of an observation, which should serve as a reasonable approximation for the effect of the ionosphere in radio interferometer images. For the ionosphere Faraday rotation calculations we use the external package RMextract (ascl:1806.024). FRion can further apply the correction to Stokes Q and U cubes to remove the effects of the ionosphere.

STELA Toolkit is a fully documented Python package for interpolating gappy/irregular, noisy light curves using Gaussian Processes, enabling the computation of a wide range of time-domain and frequency-domain data products. STELA supports standard Fourier frequency-resolved products such as power spectra, cross spectra, lag spectra, and coherence, as well as lags via the Cross-Correlation Function (CCF), interpolated with GPs or traditional linear interpolation.

DPMhalo (Descriptive Parametric Model) generates profiles of gaseous halos (pressure, electron density, and metallicity) as functions of radius, halo mass, and redshift. The code assumes spherically symmetric, volume-filling warm/hot gas, and enables mock observations of the circumgalactic medium (CGM), group halos, and clusters across a number of wavebands including X-ray, sub-millimeter/millimeter, radio, and ultraviolet (UV).

PIRATES (Polarimetric Image Reconstruction AI for Tracing Evolved Structures) uses machine learning to perform image reconstruction. It uses MCFOST (ascl:2207.023) to generate models, then uses those models to build, train, iteratively fit, and evaluate PIRATES performance.