The Astrophysics Source Code Library (ASCL) is a free online registry and repository for source codes of interest to astronomers and astrophysicists, includingÂ solar system astronomers, and lists codes that have been used in research that has appeared in, or been submitted to, peer-reviewed publications. The ASCL is indexed by the SAO/NASA Astrophysics Data System (ADS) and Web of Science and is citable by using the unique ascl ID assigned to each code. The ascl ID can be used to link to the code entry by prefacing the number with ascl.net (*i.e.*, ascl.net/1201.001).

[submitted]
Gradus.jl

Extensible spacetime agnostic general relativistic ray-tracing (GRRT): Gradus.jl is a suite of tools related to tracing geodesics and calculating observational signatures of accreting compact objects. Gradus.jl 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. Various algorithms for calculating physical quantities are implemented generically, so they may be used with different classes of spacetime with minimal effort.

[submitted]
Mosaic: Multibeamformed Observation Simulation And Interferometry Characterization

Mosaic is a software package that consists of an interferometric pattern simulator and characterizer, an optimized tiling generator and a beamforming weights calculator. It is being used in the filter-banking beamformer in MeerKAT telescope and more than 200 pulsars have been discovered from the multiple beam observations supported by Mosaic.

[ascl:2410.020]
Falcon-DM: N-body code for inspirals in DM spikes

Falcon-DM simulates intermediate mass ratio inspirals in DM spikes. This lightweight N-body code is written in C++ and is specifically tuned for simulating IMRIs embedded in dark matter (DM) spikes. It features a 2nd order Drift-Kick-Drift integrator using the symplectic HOLD scheme and symmetrized, individual, time-steps for accurate time-integration. Falcon-DM also offers post-Newtonian (PN) effects up to PN2.5 using the auxiliary velocity algorithm.

[ascl:2410.019]
Heracles: Harmonic-space statistics on the sphere

Heracles manages harmonic-space statistics on the sphere. It takes catalogs of positions and function values on the sphere and turns them into angular power spectra and mixing matrices. Heracles is both a Python library, to be used in notebooks or data processing pipelines, and a tool for running measurements from the command line using a configuration file.

[ascl:2410.018]
fastPTA: Constraining power of PTA configurations forecaster

Depta, Paul Frederik; Domcke, Valerie; Franciolini, Gabriele; Pieroni, Mauro; Babak, Stanislav; Falxa, Mikel

fastPTA forecasts the sensitivity of future Pulsar Timing Array (PTA) configurations and assesses constraints on Stochastic Gravitational Wave Background (SGWB) parameters. The code can generate mock PTA catalogs with noise levels compatible with current and future PTA experiments. These catalogs can then be used to perform Fisher forecasts of MCMC simulations.

[ascl:2410.017]
SSOF: Data-driven models for extremely precise radial velocity (EPRV) spectra

StellarSpectraObservationFitting (SSOF) measures radial velocities and creates data-driven models (with fast, physically-motivated Gaussian Process regularization) for the time-variable spectral features for both the telluric transmission and stellar spectrum measured by Extremely Precise Radial Velocity (EPRV) spectrographs (while accounting for the wavelength-dependent instrumental line-spread function). Written in Julia, SSOF provides two methods for estimating the uncertainties on the RVs and model scores based on the photon uncertainties in the original data. For quick estimates of the uncertainties, the code looks at the local curvature of the likelihood space; the second method for estimating errors is via bootstrap resampling.

[ascl:2410.016]
Gaspery: Radial velocity (RV) observing strategies

Gaspery uses the Fisher Information Matrix (FIM) to evaluate different radial velocity (RV) observing strategies; this assists observational exoplanet astronomers in constructing the observing strategy that maximizes information (or minimizes uncertainty) on the RV semi-amplitude K. The code is flexible and generalizable, however, and can maximize information on any free parameter from any model, given a time series support (x-axis).

[ascl:2410.015]
Kamodo: Space weather data access, interpolation, and visualization

Kamodo provides access to, interpolation of, and visualization of space weather models and data. The code allows model developers to represent simulation results as mathematical functions which may be manipulated directly. As the software does not generate model outputs, users must acquire the desired model outputs before these outputs can be functionalized by the software. Kamodo handles unit conversion transparently and supports interactive science discovery through Jupyter notebooks with minimal coding.

[ascl:2410.014]
CloudCovErr.jl: Debias and improve error bar estimates for photometry

CloudCovErr.jl debiases and improves error bar estimates for photometry on top of structured filamentary backgrounds. It first estimates the covariance matrix of the residuals from a previous photometric model and then computes corrections to the estimated flux and flux uncertainties. Using an infilling technique to estimate the background and its uncertainty dramatically improves flux and flux uncertainty estimates for stars in images of fields with significant nebulosity.

[ascl:2410.013]
ARK: 3D hydrodynamics code for the study of convective problems

ARK implements Computational Fluid Dynamics applications, such as Euler and all-Mach regime, on a Cartesian grid with MPI+Kokkos. It provides a performance-portable Kokkos implementation for compressible hydrodynamics and performs simulations of convection without any approximation of Boussinesq nor anelastic type. It adapts an all-Mach number scheme into a well-balanced scheme for gravity, which preserves arbitrary discrete equilibrium states up to the machine precision. The low-Mach correction in the numerical flux allows ARK to be more precise in the low-Mach regime; the code is well suited for studying highly stratified and high-Mach convective flows.