Colume (COLUMn to vOLUME) uses the statistical and spatial distribution of a column density map to infer a likely volume density distribution along each line of sight. The Python package incorporates all pre-processing (in particular re-sampling) functions needed to efficiently work on the column density maps. Colume's outputs are saved in Numpy format.

Gradus.jl traces geodesics and calculates observational signatures of accreting compact objects. The code requires only a specification of the non-zero metric components of a chosen spacetime in order to solve the geodesic equation and compute a wide variety of trajectories and orbits. Gradus includes algorithms for calculating physical quantities are implemented generically, so they may be used with different classes of spacetime with minimal effort.

Wavelets are a powerful mathematical tool whose most celebrated applications are the analysis, compression and de-noising of scientific data. JOFILUREN is a wavelet code designed for data analysis and de-noising applications. It is written in Fortran, is portable, and can also be used for studying and reducing the physical effects of particle noise in particle-mesh computer simulations. JOFILUREN is introduced in Paper I, is described in Paper II, and is further discussed in Paper III, as referenced below.

* Paper I: Romeo A. B., Horellou C. and Bergh J. (2003), "N-Body Simulations with Two-Orders-of-Magnitude Higher Performance Using Wavelets", Monthly Notices of the Royal Astronomical Society 342, 337-344
https://ui.adsabs.harvard.edu/abs/2003MNRAS.342..337R/abstract

* Paper II: Romeo A. B., Horellou C. and Bergh J. (2004), "A Wavelet Add-On Code for New-Generation N-Body Simulations and Data De-Noising (JOFILUREN)", Monthly Notices of the Royal Astronomical Society 354, 1208-1222
https://ui.adsabs.harvard.edu/abs/2004MNRAS.354.1208R/abstract

* Paper III: Romeo A. B., Agertz O., Moore B. and Stadel J. (2008), "Discreteness Effects in ΛCDM Simulations: A Wavelet-Statistical View", The Astrophysical Journal 686, 1-12
https://ui.adsabs.harvard.edu/abs/2008ApJ...686....1R/abstract

Supplementary information is given in the readme file of JOFILUREN. A pedagogical introduction to wavelets and wavelet applications, containing several useful videos and lecture notes, is given here:
https://fy.chalmers.se/~romeo/RRY025/notes+videos/

NRPyElliptic sets up initial data (ID) for numerical relativity (NR) using the same numerical methods employed for solving hyperbolic evolution equations. The code implements a hyperbolic relaxation method to solve complex nonlinear elliptic PDEs for NR ID. The hyperbolic PDEs are evolved forward in (pseudo)time, resulting in an exponential relaxation of the arbitrary initial guess to a steady state that coincides with the solution of the elliptic system. The package solves these equations on highly efficient numerical grids exploiting underlying symmetries in the physical scenario. NRPyElliptic is built in the NRPy+ (ascl:1807.025) framework, which facilitates the solution of hyperbolic PDEs on Cartesian-like, spherical-like, cylindrical-like, or bispherical-like numerical grids.

SCONE (Supernova Classification with a Convolutional Neural Network) classifies supernovae (SNe) by type using multi-band photometry data (lightcurves) using a convolutional neural networks. SCONE takes in supernova (SN) photometry data in the format output by SNANA simulations, separated into two types of files: metadata and observation data. Photometric data is pre-processed via 2D Gaussian process regression, which smooths over irregular sampling rates between filters and also allows SCONE to be independent of the filter set on which it was trained.

R2D2 (Residual-to-Residual DNN series for high-Dynamic range imaging) performs synthesis imaging for radio interferometry. The R2D2 algorithm takes a hybrid structure between a Plug-and-Play (PnP) algorithm and a learned version of the well-known Matching Pursuit algorithm. Its reconstruction is formed as a series of residual images, iteratively estimated as outputs of iteration-specific Deep Neural Networks (DNNs), each taking the previous iteration’s image estimate and associated back-projected data residual as inputs. The primary application of the R2D2 algorithm is to solve large-scale high-resolution high-dynamic range inverse problems in radio astronomy, more specifically 2D planar monochromatic intensity imaging.

LeR calculates detectable rates of gravitational waves events (both lensed and un-lensed events). Written in Python, it performs statistical simulation and forecasting of gravitational wave (GW) events and their rates. The code samples gravitational wave source properties and lens galaxies attributes and source redshifts, and can generate image properties such as source position, magnification, and time delay. The package also calculates detectable merger rates per year. Key features of LeR include efficient sampling, optimized SNR calculations, and systematic archiving of results. LeR is tailored to support both GW population study groups and GW lensing research groups by providing a comprehensive suite of tools for GW event analysis.

GalClass facilitates visual morphological classifications of large samples of galaxies taking advantage of multi-wavelength imaging and ancillary information. It offers a versatile Graphic User Interface (GUI), which adapts to the provided classification scheme. It displays a series of pre-prepared PDF files for classification, grouping by galaxy and filter, while also listing relevant metadata and displaying a color image of each source. It enables easy navigation through the sample and continuously outputs classification results in a JSON file. Finally, it offers an analysis submodule which combines and processes output files of multiple classifications.

MultiREx is a Python library designed to generate synthetic transmission spectra of exoplanets. This tool extends the functionalities of the TauREx framework, enabling the mass production of spectra and observations with added noise. The package was originally conceived to train machine learning models in the identification of biosignatures in noisy spectra.

A Python package for helping with detecting exoplanet transit dips in TESS light curves.

Features:
Load and clean .fits light curve or target pixel files
Visualize light curves and transit candidates
Designed for students, researchers, and citizen scientists
Easily download and process raw data in bulk
Easily shortlist potential exoplanet candidates

AstroPT is an autoregressive pretrained transformer developed with astronomical use-cases in mind. We have trained a selection of foundation models of increasing size from 1 million to 2.1 billion parameters on DESI legacy jpeg imagery, and find that AstroPT follows a similar saturating log-log scaling law to textual models. We also find that the models' performances on downstream tasks as measured by linear probing improves with model size up to the model parameter saturation point. We believe that collaborative community development paves the best route towards realising an open source `Large Observation Model' -- a model trained on data taken from the observational sciences at the scale seen in natural language processing. To this end, we release the source code, weights, and dataset for AstroPT under the MIT license, and invite potential collaborators to join us in collectively building and researching these models.

The IDL code PDQ predicts the positions on the sky of high-redshift quasars that should provide photons that are both acausal and uncorrelated. The best dates and times for viewing those simultaneously with Gemini two-channel `Alopeke/Zorro imagers (for high-framerate photometry) in the coming semester are reported. The predicted signal-to-noise ratios are calculated at framerate sufficient for random-number generation input to a loophole-free Bell test, and are calibrated against a public archival dataset of four pairs of highly-separated bright stars observed simultaneously (and serendipitously) at 17 Hz with that same instrumentation in 2019 to 2021. This is the code described in "Observability of Acausal and Uncorrelated Optical-Quasar Pairs for Quantum-Mechanical Experiments" published in Universe. Running the default configuration of the code will produce all the tables and plots reported there.