K2PS is an Oxford K2 planet search pipeline. Written in Python, it searches for transit-like signals from the k2sc-detrended light curves.

k2photometry reads, reduces and detrends K2 photometry and searches for transiting planets. MAST database pixel files are used as input; the output includes raw lightcurves, detrended lightcurves and a transit search can be performed as well. Stellar variability is not typically well-preserved but parameters can be tweaked to change that. The BLS algorithm used to detect periodic events is a Python implementation by Ruth Angus and Dan Foreman-Mackey (https://github.com/dfm/python-bls).

K2mosaic stitches the postage stamp-sized pixel masks obtained by NASA's Kepler and K2 missions together into CCD-sized mosaics and movies. The command-line tool's principal use is to take a set of Target Pixel Files (TPF) and turn them into more traditional FITS image files -- one per CCD channel and per cadence. K2mosaic can also be used to create animations from these mosaics. The mosaics produced by K2mosaic also makes the analysis of certain Kepler/K2 targets, such as clusters and asteroids, easier. Moreover such mosaics are useful to reveal the context of single-star observations, e.g., they enable users to check for the presence of instrumental noise or nearby bright objects.

K2fov allows users to transform celestial coordinates into K2's pixel coordinate system for the purpose of preparing target proposals and field of view visualizations. In particular, the package, written in Python, adds the "K2onSilicon" and "K2findCampaigns" tools to the command line, allowing the visibility of targets to be checked in a user-friendly way.

K2flix makes it easy to inspect the CCD pixel data obtained by NASA's Kepler space telescope. The two-wheeled extended Kepler mission, K2, is affected by new sources of systematics, including pointing jitter and foreground asteroids, that are easier to spot by eye than by algorithm. The code takes Kepler's Target Pixel Files (TPF) as input and turns them into contrast-stretched animated gifs or MPEG-4 movies. K2flix can be used both as a command-line tool or using its Python API.

Since early 2018, the Kepler/K2 project has been performing a uniform global reprocessing of data from K2 Campaigns 0 through 14. Subsequent K2 campaigns (C15-C19) are being processed using the same processing pipeline. One of the major benefits of the reprocessing effort is that, for the first time, short-cadence (1-min) light curves are produced in addition to the standard long-cadence (30-min) light curves. Users have been cautioned that the Kepler pipeline detrending module (PDC), developed for use on original Kepler data, has not been tailored for use on short-cadence K2 observations. Systematics due to events on fast timescales, such as thruster firings, are sometimes poorly corrected for many short-cadence targets. A Python data visualization and manipulation tool, called Kepler-K2 Cadence Events, has been developed that identifies and removes cadences associated with problematic thruster events, thus producing better light curves. Kepler-K2 Cadence Events can be used to visualize and manipulate light curve files and target pixel files from the Kepler, K2, and TESS missions. This software is available at the following NASA GitHub repository https://github.com/nasa/K2CE .

K2-CPM captures variability while preserving transit signals in Kepler data. Working at the pixel level, the model captures very fine-grained information about the variation of the spacecraft. The CPM models the systematic effects in the time series of a pixel using the pixels of many other stars and the assumption that any shared signal in these causally disconnected light curves is caused by instrumental effects. The target star's future and past are used and the data points are separated into training and test sets to ensure that information about any transit is perfectly isolated from the model. The method has four tuning parameters, the number of predictor stars or pixels, the autoregressive window size, and two L2-regularization amplitudes for model components, and consistently produces low-noise light curves.

Inpainting is a technique for dealing with gaps in time series data, as frequently occurs in asteroseismology data, that may generate spurious peaks in the power spectrum, thus limiting the analysis of the data. The inpainting method, based on a sparsity prior, judiciously fills in gaps in the data, preserving the asteroseismic signal as far as possible. This method can be applied both on ground and space-based data. The inpainting technique improves the oscillation modes detection and estimation, the impact of the observational window function is reduced, and the interpretation of the power spectrum is simplified. K-Inpainting can be used to study very long time series of many stars because its computation is very fast.

JWST_Simulation generates a novel geometric-focused deep field simulation of the expected JWST future deep field image. Galaxies are represented by ellipses with randomly-generated positions and orientations. Three scripts are included: a deterministic simulation, an ensemble simulation, and a more-realistic monochrome image simulation. The following initial conditions can be perturbed in these codes: H0, Ωm, ΩΛ, the dark energy equation of state parameter, the number of unseen galaxies in the Hubble Ultra Deep Field Image (HUDF), the increase in effective radius due to the JWST’s higher sensitivity, the anisotropy of dark energy, and the maximum redshift reached by the JWST. Galaxy number densities are estimated using integration over comoving volume with an integration constant calibrated with the Hubble Ultra Deep Field. A galaxy coverage percentage is calculated for each image to determine the percentage of the background occupied by galaxies.

JWFront visualizes wavefronts and light cones in general relativity. The interactive front-end allows users to enter the initial position values and choose the values for mass and angular momentum per unit mass. The wavefront animations are available in 2D and 3D; the light cones are visualized using the coordinate systems (t, x, y) or (t, z, x). JWFront can be easily modified to simulate wavefronts and light cones for other spacetime by providing the Christoffel symbols in the program.

JVarStar (Java Variable Star Analysis) performs pattern classification by analyzing variable star data. This all-in-one library package includes machine learning techniques, fundamental mathematical methods, and digital signal processing functions that can be externally referenced (i.e., from Python), or can be used for further Java development. This library has dependencies on several open source packages that, along with the developed functionality, provides a developer with an easily accessible library from which to construct stable variable star analysis and classification code.

Juwvid performs time-frequency analysis. Written in Julia, it uses a modified version of the Wigner distribution, the pseudo Wigner distribution, and the short-time Fourier transform from MATLAB GPL programs, tftb-0.2. The modification includes the zero-padding FFT, the non-uniform FFT, the adaptive algorithm by Stankovic, Dakovic, Thayaparan 2013, the S-method, the L-Wigner distribution, and the polynomial Wigner-Ville distribution.

Jupiter is a multidimensional astrophysical hydrocode. It is based on a Godunov method, and it is parallelized with MPI. The mesh geometry can either be cartesian, cylindrical or spherical. It allows mesh refinement and includes special features adapted to the description of planets embedded in disks and nearly steady states.

Juliet essentially serves as a wrapper of other tools, including Batman (ascl:1510.002), George (ascl:1511.015), Dynesty (ascl:1809.013) and AstroPy (ascl:1304.002), to analyze and model transits, radial-velocities, or both from multiple instruments at the same time. Using nested sampling algorithms, it performs a thorough sampling of the parameter space and a model comparison via Bayesian evidences. Juliet also fits transiting and non-transiting multi-planetary systems, and Gaussian Processes (GPs) which might share hyperparameters between the photometry and radial-velocities simultaneously (e.g., stellar rotation periods).

JUDE (Jayant's UVIT Data Explorer) converts the Level 1 data (FITS binary table) from the Ultraviolet Imaging Telescope (UVIT) on ASTROSAT into three output files: a photon event list as a function of frame number (FITS binary table); a FITS image file with two extensions; and a PNG file created from the FITS image file with an automated scaling.

JSPAM models galaxy collisions using a restricted n-body approach to speed up computation. Instead of using a softened point-mass potential, the software supports a modified version of the three component potential created by Hernquist (1994, ApJS 86, 389). Although spherically symmetric gravitationally potentials and a Gaussian model for the bulge are used to increase computational efficiency, the potential mimics that of a fully consistent n-body model of a galaxy. Dynamical friction has been implemented in the code to improve the accuracy of close approaches between galaxies. Simulations using this code using thousands of particles over the typical interaction times of a galaxy interaction take a few seconds on modern desktop workstations, making it ideal for rapidly prototyping the dynamics of colliding galaxies. Extensive testing of the code has shown that it produces nearly identical tidal features to those from hierarchical tree codes such as Gadget but using a fraction of the computational resources. This code was used in the Galaxy Zoo: Mergers project and is very well suited for automated fitting of galaxy mergers with automated pattern fitting approaches such as genetic algorithms. Java and Fortran versions of the code are available.

JPLephem loads and uses standard Jet Propulsion Laboratory (JPL) ephemerides for predicting the position and velocity of a planet or other Solar System body. It is one of the foundations of the Skyfield (ascl:1907.024) astronomy library for Python, and can also be used as a standalone package to generate raw vectors.

FITS File interaction written in Visual Studio C# .Net.

JPFITS is not based upon any other implementation and is written from the ground-up, consistent with the FITS standard, designed to interact with FITS files as object-oriented structures.

JPFITS provides functionality to interact with FITS images and binary table extensions, as well as providing common mathematical methods for the manipulation of data, data reductions, profile fitting, photometry, etc.

JPFITS also implements object-oriented classes for Point Source Extraction, World Coordinate Solutions (WCS), WCS automated field solving, FITS Headers and Header Keys, etc.

The automatic world coordinate solver is based on the trigonometric algorithm as described here:

https://iopscience.iop.org/article/10.1088/1538-3873/ab7ee8

All function parameters, methods, properties, etc., are coded with XML descriptions which will function with Visual Studio. Other code editors may or may not read the XML files.

Everything which is reasonable to parallelize in order to benefit from the computation speed increase for multi-threaded systems has been done so. In all such cases function options are given in order to specify the use of parallelism or not. Generally, most image manipulation functions are highly amenable to parallelism. No parallelism is forced, i.e., any code which may execute parallelized is given a user option to do so or not.

JoXSZ jointly fits the thermodynamic profiles of galaxy clusters from both SZ and X-ray data using a Markov chain Monte Carlo fitting algorithm. It is an enhanced version of preprofit (ascl:1910.002), which fits only SZ data. JoXSZ parameterizes the pressure and electron density profile of a galaxy cluster with a given center and derives the temperature profile as the ratio of these quantities through the ideal gas law. The X-ray and SZ-based temperatures can be similar or different, which allows study of the cluster elongation along line of sight, gas clumping, or calibration uncertainties.

JOFILUREN analyzes and de-noises scientific data and is useful for studying and reducing the physical effects of particle noise in particle-mesh computer simulations. It uses wavelets, which can efficiently remove noise from cosmological, galaxy and plasma N-body simulations. Written in Fortran, the code is portable and can be included in grid-based N-body codes. JOFILUREN can also be applied for removing noise from standard data, such as signals and images.

jnkepler models photometric and radial velocity data of multi-planet systems via N-body integration. Built with JAX, it leverages automatic differentiation for efficient computation of model gradients. This enables seamless integration with gradient-based optimizers and Hamiltonian Monte Carlo methods, including the No-U-Turn Sampler (NUTS) in NumPyro (ascl:2505.005). jnkepler is particularly suited for efficiently sampling from multi-planet posteriors involving a larger number of parameters and strong degeneracy.

JKTLD outputs theoretically-calculated limb darkening (LD) strengths for equations (LD laws) which predict the amount of LD as a function of the part of the star being observed. The coefficients of these laws are obtained by bilinear interpolation (in effective temperature and surface gravity) in published tables of coefficients calculated from stellar model atmospheres by several researchers. Many observations of stars require the strength of limb darkening (LD) to be estimated, which can be done using theoretical models of stellar atmospheres; JKTLD can help in these circumstances.

The JKTEBOP code is used to fit a model to the light curves of detached eclipsing binary stars in order to derive the radii of the stars as well as various other quantities. It is very stable and includes extensive Monte Carlo or bootstrapping error analysis algorithms. It is also excellent for transiting extrasolar planetary systems. All input and output is done by text files; JKTEBOP is written in almost-standard FORTRAN 77 using first the g77 compiler and now the ifort compiler.

Jitter predicts radial-velocity (RV) jitter due to stellar oscillations and granulation, in terms of various sets of fundamental stellar properties. The code can also be used to set a prior for the jitter term as a component when modeling the Keplerian orbits of the exoplanets.

JHelioview is open source visualization software for solar physics data. The JHelioviewer client application enables users to browse petabyte-scale image archives; the JHelioviewer server integrates a JPIP server, metadata catalog, and an event server. JHelioview uses the JPEG 2000 image compression standard, which provides efficient access to petabyte-scale image archives; JHelioviewer also allows users to locate and manipulate specific data sets.

JexoSim 2.0 (JWST Exoplanet Observation Simulator) simulates exoplanet transit observations using all four instruments of the James Webb Space Telescope, and is designed for the planning and validation of science cases for JWST. The code generates synthetic spectra that capture the full impact of complex noise sources and systematic trends, allowing for assessment of both accuracy and precision in the final spectrum. JexoSim does not contain all known systematics for the various instruments, but is a good starting point to investigate the effects of systematics, and has the framework to incorporate more systematics in the future.

jetsimpy creates hydrodynamic simulations of relativistic blastwaves with tabulated angular energy and Lorentz factor profiles and efficiently models Gamma-Ray Burst afterglows. It supports tabulated angular energy and tabulated angular Lorentz factor profiles. jetsimpy also supports ISM, wind, and mixed external density profile, including synthetic afterglow light curves, apparent superluminal motion, and sky map and Gaussian equivalent image sizes. Additionally, you can add your own emissivity model by defining a lambda function in a c++ source file, allowing the package to be used for more complicated models such as Synchrotron self-absorption.

JetSeT reproduces radiative and accelerative processes acting in relativistic jets and fits the numerical models to observed data. This C/Python framework re-bins observed data, can define data sets, and binds to astropy tables and quantities. It can use Synchrotron Self-Compton (SSC), external Compton (EC) and EC against the CMB when defining complex numerical radiative scenarios. JetSeT can constrain the model in the pre-fitting stage based on accurate and already published phenomenological trends starting from parameters such as spectral indices, peak fluxes and frequencies, and spectral curvatures. The package fits multiwavelength SEDs using both frequentist approach and Bayesian MCMC sampling, and also provides self-consistent temporal evolution of the plasma under the effect of radiative and accelerative processes for both first order and second order (stochastic acceleration) processes.

JETGET accesses, visualizes, and analyses (magnetized-)fluid dynamics data stored in Hierarchical Data Format (HDF) and ASCII files. Although JETGET has been optimized to handle data output from jet simulations using the Zeus code (ascl:1306.014) from NCSA, it is also capable of analyzing other data output from simulations using other codes. JETGET can select variables from the data files, render both two- and three-dimensional graphics and analyze and plot important physical quantities. Graphics can be saved in encapsulated Postscript, JPEG, VRML, or saved into an MPEG for later visualization and/or presentations. The strength of JETGET in extracting the physics underlying such phenomena is demonstrated as well as its capabilities in visualizing the 3-dimensional features of the simulated magneto-hydrodynamic jets. The JETGET tool is written in Interactive Data Language (IDL) and uses a graphical user interface to manipulate the data. The tool was developed on a LINUX platform and can be run on any platform that supports IDL.

Written in Python, JetCurry models the 3D geometry of jets from 2-D images. JetCurry requires NumPy and SciPy and incorporates emcee (ascl:1303.002) and AstroPy (ascl:1304.002), and optionally uses VPython. From a defined initial part of the jet that serves as a reference point, JetCurry finds the position of highest flux within a bin of data in the image matrix and fits along the x axis for the general location of the bends in the jet. A spline fitting is used to smooth out the resulted jet stream.

JET (JWST Exoplanet Targeting) optimizes lists of exoplanet targets for atmospheric characterization by the James Webb Space Telescope (JWST). The software uses catalogs of planet detections, either simulated, or actual and categorizes targets by radius and equilibrium temperature; it also estimates planet masses and generates model spectra and simulated instrument spectra. JET then performs a statistical analysis to determine if the instrument spectra can confirm an atmospheric detection and finally ranks the targets within each category by observation time required for detection.

JEDI searches for and characterizes coronal dimming in light curves produced from the Solar Dynamics Observatory (SDO) Extreme Ultraviolet (EUV) Variability Experiment (EVE). The suite has a wrapper script that calls other functions, which can also be run independently assuming needed inputs from prior functions are provided. JEDI's functions fit light curves and return the best fit, compute precision for iron light curves, and find the biggest dimming depth and its time in a given light curve. JEDI also includes functions for finding the duration of the dimming, minimum, maximum, and mean slope of dimming of a light curve, and for identifying the biggest peak in two light curves, time shifting them so the peaks are concurrent, scaling them so the peaks are the same magnitude, and then subtracting them, among other useful functions.

Jdaviz provides data viewers and analysis plugins that can be flexibly combined as desired to create interactive applications. It offers Specviz (ascl:1902.011) for visualization and quick-look analysis of 1D astronomical spectra; Mosviz for visualization of astronomical spectra, including 1D and 2D spectra as well as contextual information, and Cubeviz for visualization of spectroscopic data cubes (such as those produced by JWST MIRI). Imviz, which provides visualization and quick-look analysis for 2D astronomical images, is also included. Jdaviz is designed with instrument modes from the James Webb Space Telescope (JWST) in mind, but the tool is flexible enough to read in data from many astronomical telescopes, and the documentation provides a complete table of all supported modes.

JCMTDR reduces continuum on-the-fly mapping data obtained with UKT14 or the heterodyne instruments using the IFD on the James Clerk Maxwell Telescope. This program reduces archive data and heterodyne beam maps and was distributed as part of the Starlink software collection (ascl:1110.012).

COADD was used to reduce photometry and continuum data from the UKT14 instrument on the James Clerk Maxwell Telescope in the 1990s. The software can co-add multiple observations and perform sigma clipping and Kolmogorov-Smirnov statistical analysis. Additional information on the software is available in the JCMT Spring 1993 newsletter (large PDF).

JB2008 (Jacchia-Bowman 2008) is an empirical thermospheric density model developed as an improved revision to the Jacchia-Bowman 2006 model, based on Jacchia’s diffusion equations. Driving solar indices are computed from on-orbit sensor data, which are used for the solar irradiances in the extreme through far ultraviolet, including x-ray and Lyman-α wavelengths. Exospheric temperature equations are developed to represent the thermospheric EUV and FUV heating. Semiannual density equations based on multiple 81-day average solar indices are used to represent the variations in the semiannual density cycle that result from EUV heating, and geomagnetic storm effects are modeled using the Dst index as the driver of global density changes.

jaxspec performs statistical inference on X-ray spectra. It loads an X-ray spectrum (in the OGIP standard), defines a spectral model from the implemented components, and calculates the best parameters using state-of-the-art Bayesian approaches. The code is built on top of JAX (ascl:2111.002) to provide just-in-time compilation and automatic differentiation of the spectral models, enabling the use of sampling algorithms. jaxspec is written in pure Python and is not dependent on HEASoft (ascl:1408.004).

jaxoplanet is a functional-programming-forward implementation of many features from the exoplanet and starry packages built on top of JAX (ascl:2111.002). It includes fast and robust implementations of many exoplanet-specific operations, including solving Kepler’s equation, and computing limb-darkened light curves. jaxoplanet has first-class support for hardware acceleration using GPUs and TPUs, and integrates seamlessly with modeling tools such as NumPyro (ascl:2505.005) and Flax (ascl:2504.026).

JAX brings Autograd and XLA together for high-performance machine learning research. It can automatically differentiate native Python and NumPy functions. The code can differentiate through loops, branches, recursion, and closures, and it can take derivatives of derivatives of derivatives. JAX supports reverse-mode differentiation (a.k.a. backpropagation) via grad as well as forward-mode differentiation, and the two can be composed arbitrarily to any order.

JAVELIN (formerly known as SPEAR) is an approach to reverberation mapping that computes the lags between the AGN continuum and emission line light curves and their statistical confidence limits. It uses a damped random walk model to describe the quasar continuum variability and the ansatz that emission line variability is a scaled, smoothed and displaced version of the continuum. While currently configured only to simultaneously fit light curve means, it includes a general linear parameters formalism to fit more complex trends or calibration offsets. The noise matrix can be modified to allow for correlated errors, and the correlation matrix can be modified to use a different stochastic process. The transfer function model is presently a tophat, but this can be altered by changing the line-continuum covariance matrices. It is also able to cope with some problems in traditional reverberation mapping, such as irregular sampling, correlated errors and seasonal gaps.

The Jeans Anisotropic MGE (JAM) modeling method uses the Multi-Gaussian Expansion parameterization for the galaxy surface brightness. The code allows for orbital anisotropy (three-integrals distribution function) and also provides the full second moment tensor, including proper motions and radial velocities.

JAGS analyzes Bayesian hierarchical models using Markov Chain Monte Carlo (MCMC) simulation not wholly unlike BUGS. JAGS has three aims:

- to have a cross-platform engine for the BUGS language;
- to be extensible, allowing users to write their own functions, distributions and samplers; and
- to be a platform for experimentation with ideas in Bayesian modeling.

J-comb combines high-resolution data with large-scale missing information with low-resolution data containing the short spacing. Based on uvcombine (ascl:2208.014), it takes as input FITS files of low- and high-resolution images, the angular resolution of the input images, and the pixel size of the input images, and outputs a FITS file of the combined image.

J plots classifies and quantifies a pixelated structure, based on its principal moments of inertia, thus enabling automatic detection and objective comparisons of centrally concentrated structures (cores), elongated structures (filaments) and hollow circular structures (bubbles) from the main population of slightly irregular blobs that make up most astronomical images. Examples of how to analyze 2D or 3D datasets, enabling an unbiased analysis and comparison of simulated and observed structures are provided along with the Python code.

The simulation and analysis framework ixpeobssim was specifically developed for the Imaging X-ray Polarimetry Explorer (IXPE). It produces realistic simulated observations, in the form of event lists in FITS format, that also contain a strict superset of the information included in the publicly released IXPE data products. The framework's core simulation capabilities are complemented by post-processing applications that support the spatial, spectral, and temporal models needed for analysis of typical polarized X-ray sources, allowing implementation of complex, polarization-aware analysis pipelines. Where applicable, the data formats are consistent with the common display and analysis tools used by the community, e.g., the binned count spectra can be fed into XSPEC (ascl:9910.005), along with the corresponding response functions, for doing standard spectral analysis. All ixpeobssim simulation and analysis tools are fully configurable via the command line.

iWander assesses the origin of interstellar small bodies such as asteroids and comets. It includes a series of databases and tools that can be used in general for studying the dynamics of an interstellar vagabond object (small−body, interstellar spaceship and even stars).

IUEDR reduces IUE data. It addresses the problem of working from the IUE Guest Observer tape or disk file through to a calibrated spectrum that can be used in scientific analysis and is a complete system for IUE data reduction. IUEDR was distributed as part of the Starlink software collection (ascl:1110.012).

ITERA, the IDL Tool for Emission-line Ratio Analysis, is an IDL widget tool that allows you to plot ratios of any strong atomic and ionized emission lines as determined by standard photoionization and shock models. These "line ratio diagrams" can then be used to determine diagnostics for nebulae excitation mechanisms or nebulae parameters such as density, temperature, metallicity, etc. ITERA can also be used to determine line sensitivities to such parameters, compare observations with the models, or even estimate unobserved line fluxes.

ISW and Weak Lensing Likelihood code is the likelihood code that calculates the likelihood of Integrated Sachs Wolfe and Weak Lensing of Cosmic Microwave Background using the WMAP 3year CMB maps with mass tracers such as 2MASS (2-Micron All Sky Survey), SDSS LRG (Sloan Digital Sky Survey Luminous Red Galaxies), SDSS QSOs (Sloan Digital Sky Survey Quasars) and NVSS (NRAO VLA All Sky Survey) radio sources. The details of the analysis (*thus the likelihood code) can be understood by reading the papers ISW paper and Weak lensing paper. The code does brute force theoretical matter power spectrum and calculations with CAMB (ascl:1102.026). See the paper for an introduction, descriptions, and typical results from some pre-WMAP data. The code is designed to be integrated into CosmoMC.

The ISPy3 suite of Python routines models and analyzes integrated-light spectra of stars and stellar populations. The actual spectral modeling and related tasks such as setting up model atmospheres is done via external codes. Currently, the Kurucz codes (ATLAS/SYNTHE) and MARCS/TurboSpectrum are supported, though implementing other similar codes should be relatively straight forward.