EzGal is a flexible Python program which generates observable parameters (magnitudes, colors, and mass-to-light ratios) for arbitrary input stellar population synthesis (SPS) models; it enables simple, direct comparison of different model sets so that the uncertainty introduced by choice of model set can be quantified. EzGal is also capable of generating composite stellar population models (CSPs) for arbitrary input star-formation histories and reddening laws, and can be used to interpolate between metallicities for a given model set.

EZ (Easy-Z) estimates redshifts for extragalactic objects. It compares the observed spectrum with a set of (user given) spectral templates to find out the best value for the redshift. To accomplish this task, it uses a highly configurable set of algorithms. EZ is easily extendible with new algorithms. It is implemented as a set of C programs and a number of python classes. It can be used as a standalone program, or the python classes can be directly imported by other applications.

EZ_Ages is an IDL code package that computes the mean, light-weighted stellar population age, [Fe/H], and abundance enhancements [Mg/Fe], [C/Fe], [N/Fe], and [Ca/Fe] for unresolved stellar populations. This is accomplished by comparing Lick index line strengths between the data and the stellar population models of Schiavon (2007), using a method described in Graves & Schiavon (2008). The algorithm uses the inversion of index-index model grids to determine ages and abundances, and exploits the sensitivities of the various Lick indices to measure Mg, C, N, and Ca enhancements over their solar abundances with respect to Fe.

In EyE (Enhance Your Extraction) an artificial neural network connected to pixels of a moving window (retina) is trained to associate these input stimuli to the corresponding response in one or several output image(s). The resulting filter can be loaded in SExtractor (ascl:1010.064) to operate complex, wildly non-linear filters on astronomical images. Typical applications of EyE include adaptive filtering, feature detection and cosmetic corrections.

Extreme-deconvolution is a general algorithm to infer a d-dimensional distribution function from a set of heterogeneous, noisy observations or samples. It is fast, flexible, and treats the data's individual uncertainties properly, to get the best description possible for the underlying distribution. It performs well over the full range of density estimation, from small data sets with only tens of samples per dimension, to large data sets with hundreds of thousands of data points.

extrapops simulates extra-galactic populations of gravitational waves sources and models their emission during the inspiral phase. The code approximately assesses the detectability of individual sources by LISA and computes the background due to unresolved sources in the LISA band using different methods. The simulated populations can be saved in a format compatible with LISA LDC. Simulations are well calibrated to produce accurate background calculations and fair random generation at the tails of the distributions, which is important for accurate probability of detectable events. extrapops uses a number of ad-hoc techniques for rapid simulation and allows room for further optimization up to almost 1 order of magnitude.

ExtLaw_H18 generates the extinction law between 0.8 - 2.2 microns. The law is derived using the Westerlund 1 (Wd1) main sequence (A_Ks ~ 0.6 mag) and Arches cluster field Red Clump at the Galactic Center (A_Ks ~ 2.7 mag). To derive the law a Wd1 cluster age of 5 Myr is assumed, though changing the cluster age between 4 Myr -- 7 Myr has no effect on the law. This extinction law can be applied to highly reddened stellar populations that have similar foreground material as Wd1 and the Arches RC, namely dust from the spiral arms of the Milky Way in the Galactic Plane.

extinction is an implementation of fast interstellar dust extinction laws in Python. It contains Cython-optimized implementations of empirical dust extinction laws found in the literature. Flux values can be reddened or dereddened using included functions, and all extinction laws accept a unit keyword to change the interpretation of the wavelength array from Angstroms to inverse microns. Part of this code originated in the specutils package (ascl:1902.012).

Extinction-distances uses the number of foreground stars and a Galactic model of the stellar distribution to estimate the distance to dark clouds. It exploits the relatively narrow range of intrinsic near-infrared colors of stars to separate foreground from background stars. An advantage of this method is that the distribution of stellar colors in the Galactic model need not be precisely correct, only the number density as a function of distance from the Sun.

The program EXTINCT.FOR is a FORTRAN subroutine summarizing a three-dimensional visual Galactic extinction model, based on a number of published studies. INPUTS: Galactic latitude (degrees), Galactic longitude (degrees), and source distance (kpc). OUTPUTS (magnitudes): Extinction, extinction error, a statistical correction term, and an array containing extinction and extinction error from each subroutine. The model is useful for correcting visual magnitudes of Galactic sources (particularly in statistical models), and has been used to find Galactic extinction of extragalactic sources. The model's limited angular resolution (subroutine-dependent, but with a minimum resolution of roughly 2 degrees) is necessitated by its ability to describe three-dimensional structure.

EXSdetect is a python implementation of an X-ray source detection algorithm which is optimally designed to detected faint extended sources and makes use of Voronoi tessellation and Friend-of-Friend technique. It is a flexible tool capable of detecting extended sources down to the lowest flux levels attainable within instrumental limitations while maintaining robust photometry, high completeness, and low contamination, regardless of source morphology. EXSdetect was developed mainly to exploit the ever-increasing wealth of archival X-ray data, but is also ideally suited to explore the scientific capabilities of future X-ray facilities, with a strong focus on investigations of distant groups and clusters of galaxies.

ExPRES (Exoplanetary and Planetary Radio Emission Simulator) reproduces the occurrence of CMI-generated radio emissions from planetary magnetospheres, exoplanets or star-planet interacting systems in time-frequency plane, with special attention given to computation of the radio emission beaming at and near its source. Physical information drawn from such radio observations may include the location and dynamics of the radio sources, the type of current system leading to electron acceleration and their energy and, for exoplanetary systems, the magnetic field strength, the orbital period of the emitting body and the rotation period, tilt and offset of the planetary magnetic field. Most of these parameters can be remotely measured only via radio observations. ExPRES code provides the proper framework of analysis and interpretation for past (Cassini, Voyager, Galileo), current (Juno, ground-based radio telescopes) and future (BepiColombo, Juice) observations of planetary radio emissions, as well as for future detection of radio emissions from exoplanetary systems.

exoVista generates a "universe" of planetary systems, creating thousands of models of quasi-self-consistent planetary systems around known nearby stars at scattered light wavelengths. It efficiently records the position, velocity, spectrum, and physical parameters of all bodies as functions of time. exoVista models can be used for simulating surveys using the direct imaging, transit, astrometric, and radial velocity techniques.

The simple, straightforward Exotrending code detrends exoplanet transit light curves given a light curve (flux versus time) and good ephemeris (epoch of first transit and orbital period). The code has been tested with Kepler and K2 light curves and should work with any other light curve.

EXOTIC (EXOplanet Transit Interpretation Code) analyzes photometric data of transiting exoplanets into lightcurves and retrieves transit epochs and planetary radii. The software reduces images of a transiting exoplanet into a lightcurve, and fits a model to the data to extract planetary information crucial to increasing the efficiency of larger observational platforms. EXOTIC is written in Python and supports the citizen science project Exoplanet Watch. The software runs on Windows, Macintosh, and Linux/Unix computer, and can also be used via Google Colab.

ExoTETHyS models exoplanetary transits, eclipsing binaries, and related phenomena. The package calculates stellar limb-darkening coefficients down to <10 parts per million (ppm) and generates an exact transit light-curve based on the entire stellar intensity profile rather than limb-darkening coefficients.

ExoSOFT provides orbital analysis of exoplanets and binary star systems. It fits any combination of astrometric and radial velocity data, and offers four parameter space exploration techniques, including MCMC. It is packaged with an automated set of post-processing and plotting routines to summarize results, and is suitable for performing orbital analysis during surveys with new radial velocity and direct imaging instruments.

EXOSIMS generates and analyzes end-to-end simulations of space-based exoplanet imaging missions. The software is built up of interconnecting modules describing different aspects of the mission, including the observatory, optical system, and scheduler (encoding mission rules) as well as the physical universe, including the assumed distribution of exoplanets and their physical and orbital properties. Each module has a prototype implementation that is inherited by specific implementations for different missions concepts, allowing for the simulation of widely variable missions.

ExoSim models host star and planet transit events, simulating the temporal change in stellar flux due to the light curve. It is wavelength-dependent, using an input planet spectrum to determine the light curve depth for any given wavelength and can capture temporal effects, such as correlated noise. ExoSim's star spot simulator produces simulated observations that include spot and facula contamination. The code is flexible and can be generically applied to different instruments that simulate specific time-dependent processes.

ExoRT is a flexible, two-stream radiative transfer code that interfaces with CAM/CESM (http://www.cesm.ucar.edu/models/current.html) or 1D offline; it is also used with ExoCAM (ascl:2002.020). Quadrature is used for shortwave and hemispheric mean is used for longwave. The gas phase optical depths are calculate using a correlated K-distribution method, with overlapping bands treated using an amount weighted scheme. Cloud optics are treated using mie scattering for both liquid and ice clouds, and cloud overlap is treated using Monte Carlo Independent Column Approximation.

Exorings is suitable for surveying entire catalogs of transiting planet candidates for exoring candidates, providing a subset of objects worthy of more detailed light curve analysis. Moreover, it is highly suited for uncovering evidence of a population of ringed planets by comparing the radius anomaly and PR-effects in ensemble studies.

Exorings, written in Python, contains tools for displaying and fitting giant extrasolar planet ring systems; it uses FITS formatted data for input.

ExoRad 2.0, a generic point source radiometric model, interfaces with any instrument to provide an estimate of several Payload performance metrics. For each target and for each photometric and spectroscopic channel, the code provides estimates of signals in pixels, saturation times, and read, photon, and dark current noise. ExoRad also provides estimates for the zodiacal background, inner sanctum, and sky foreground.

ExoPriors calculates a log-likelihood penalty for an input set of transit parameters to account for observational bias (geometric and signal-to-noise ratio detection bias) of transiting exoplanets. Written in Python, the code calculates this log-likelihood penalty in one of seven user-specified cases specified with Boolean input parameters for geometric and/or SNR bias, grazing or non-grazing events, and occultation events.

Exopop is a general hierarchical probabilistic framework for making justified inferences about the population of exoplanets. Written in python, it requires that the occurrence rate density be a smooth function of period and radius (employing a Gaussian process) and takes survey completeness and observational uncertainties into account. Exopop produces more accurate estimates of the whole population than standard procedures based on weighting by inverse detection efficiency.

ExoPlex is a thermodynamically self-consistent mass-radius-composition calculator. Users input a bulk molar composition and a mass or radius, and ExoPlex will calculate the resulting radius or mass. Additionally, it will produce the planet's core mass fraction, interior mineralogy and the pressure, adiabatic temperature, gravity and density profiles as a function of depth.

ExoPlaSim extends the PlaSim (ascl:2107.019) 3D general climate model to terrestrial exoplanets. It includes the PlaSim general circulation model and modifications that allow this code to run tidally-locked planets, planets with substantially different surface pressures than Earth, planets orbiting stars with different effective temperatures, super-Earths, and more. ExoPlaSim includes the ability to compute carbon-silicate weathering, dynamic orography through the glacier module (though only accumulation and ablation/evaporation/melting are included; glacial flow and spreading are not), and storm climatology.

Exoplanet determines the posterior distribution of exoplanets by use of a trans-dimensional Markov Chain Monte Carlo method within Nested Sampling. This method finds the posterior distribution in a single run rather than requiring multiple runs with trial values.

exoplanet is a toolkit for probabilistic modeling of transit and/or radial velocity observations of exoplanets and other astronomical time series using PyMC3 (ascl:1610.016), a flexible and high-performance model building language and inference engine. exoplanet extends PyMC3's language to support many of the custom functions and distributions required when fitting exoplanet datasets. These features include a fast and robust solver for Kepler's equation; scalable Gaussian processes using celerite (ascl:1709.008); and fast and accurate limb darkened light curves using the code starry (ascl:1810.005). It also offers common reparameterizations for limb darkening parameters, and planet radius and impact parameters.

ExoPlanet provides a graphical interface for the construction, evaluation and application of a machine learning model in predictive analysis. With the back-end built using the numpy and scikit-learn libraries, ExoPlanet couples fast and well tested algorithms, a UI designed over the PyQt framework, and graphs rendered using Matplotlib. This serves to provide the user with a rich interface, rapid analytics and interactive visuals.

ExoPlanet is designed to have a minimal learning curve to allow researchers to focus more on the applicative aspect of machine learning algorithms rather than their implementation details and supports both methods of learning, providing algorithms for unsupervised and supervised training, which may be done with continuous or discrete labels. The parameters of each algorithms can be adjusted to ensure the best fit for the data. Training data is read from a CSV file, and after training is complete, ExoPlanet automates the building of the visual representations for the trained model. Once training and evaluation yield satisfactory results, the model may be used to make data based predictions on a new data set.

ExoPix is a collection of tutorials aimed at illustrating the imaging of exoplanets with the James Webb Space Telescope (JWST). ExoPix tutorials are meant to demonstrate the application of the PSF-subtraction algorithm pyKLIP (ascl:1506.001) to simulated JWST NIRCAM data. We provide simple walkthroughs of pyKLIP’s ability to reveal exoplanets, compute contrast curves, and measure exoplanet astrometry and photometry in imaged extrasolar systems.

ExoJAX provides auto-differentiable line-by-line spectral modeling of exoplanets/brown dwarfs/M dwarfs using JAX (ascl:2111.002). In a nutshell, ExoJAX allows the user to do a HMC-NUTS fitting using the latest molecular/atomic data in ExoMol, HITRAN/HITEMP, and VALD3. The code enables a fully Bayesian inference of the high-dispersion data to fit the line-by-line spectral computation to the observed spectrum, from end-to-end (i.e. from molecular/atomic databases to real spectra), by combining it with the Hamiltonian Monte Carlo in recent probabilistic programming languages such as NumPyro.

exoinformatics computes the entropy of a planetary system's size ordering using three different entropy methods: tally-scores, integral path, and change points.

ExoGAN (Exoplanets Generative Adversarial Network) analyzes exoplanetary atmospheres using an unsupervised deep-learning algorithm that recognizes molecular features, atmospheric trace-gas abundances, and planetary parameters. After training, ExoGAN can be applied to a large number of instruments and planetary types and can be used either as a final atmospheric analysis or to provide prior constraints to subsequent retrieval.

ExoFit is a freely available software package for estimating orbital parameters of extra-solar planets. ExoFit can search for either one or two planets and employs a Bayesian Markov Chain Monte Carlo (MCMC) method to fit a Keplerian radial velocity curve onto the radial velocity data.

EXOFASTv2 improves upon EXOFAST (ascl:1207.001) for exoplanet modeling. It uses a differential evolution Markov Chain Monte Carlo code to fit an arbitrary number of transits (each with their own error scaling, normalization, TTV, and/or detrending parameters), an arbitrary number of RV sources (each with their own zero point and jitter), and an arbitrary number of planets, changing nothing but command line arguments and configuration files. The global model includes integrated isochrone and SED models to constrain the stellar properties and can accept priors on any fitted or derived quantities (e.g., parallax from Gaia). It is easily extensible to add additional effects or parameters.

EXOFAST is a fast, robust suite of routines written in IDL which is designed to fit exoplanetary transits and radial velocity variations simultaneously or separately, and characterize the parameter uncertainties and covariances with a Differential Evolution Markov Chain Monte Carlo method. Our code self-consistently incorporates both data sets to simultaneously derive stellar parameters along with the transit and RV parameters, resulting in consistent, but tighter constraints on an example fit of the discovery data of HAT-P-3b that is well-mixed in under two minutes on a standard desktop computer. EXOFAST has an easy-to-use online interface for several basic features of our transit and radial velocity fitting. A more robust version of EXOFAST, EXOFASTv2 (ascl:1710.003), is also available.

Exodetbox provides mathematical methods for calculating the planet-star separation and difference in magnitude extrema as well as when planets have particular planet-star separations or differences in magnitude. The code also projects the 3D Keplerian Orbit into a reparameterized 2D ellipse in the plane of the sky. Exodetbox is implemented in the EXOSIMS modeling software (ascl:1706.010).

ExoData is a python interface for accessing and exploring the Open Exoplanet Catalogue. It allows searching of planets (including alternate names) and easy navigation of hierarchy, parses spectral types and fills in missing parameters based on programmable specifications, and provides easy reference of planet parameters such as GJ1214b.ra, GJ1214b.T, and GJ1214b.R. It calculates values such as transit duration, can easily rescale units, and can be used as an input catalog for large scale simulation and analysis of planets.

The Exoplanet Characterization ToolKit (ExoCTK) focuses primarily on the atmospheric characterization of exoplanets and provides tools for time-series observation planning, forward modeling, data reduction, limb darkening, light curve fitting, and retrievals. It contains calculators for contamination, visibility, integrations and groups, and includes several Jupyter Notebooks to aid in learning how to use the various tools included in the ExoCTK package.

ExoCross generates spectra and thermodynamic properties from molecular line lists in ExoMol, HITRAN, or several other formats. The code is parallelized and also shows a high degree of vectorization; it works with line profiles such as Doppler, Lorentzian and Voigt and supports several broadening schemes. ExoCross is also capable of working with the recently proposed method of super-lines. It supports calculations of lifetimes, cooling functions, specific heats and other properties. ExoCross converts between different formats, such as HITRAN, ExoMol and Phoenix, and simulates non-LTE spectra using a simple two-temperature approach. Different electronic, vibronic or vibrational bands can be simulated separately using an efficient filtering scheme based on the quantum numbers.

exocartographer solves the exo-cartography inverse problem. This flexible forward-modeling framework, written in Python, retrieves the albedo map and spin geometry of a planet based on time-resolved photometry; it uses a Markov chain Monte Carlo method to extract albedo maps and planet spin and their uncertainties. Gaussian Processes use the data to fit for the characteristic length scale of the map and enforce smooth maps.

ExoCAM adapts the NCAR Community Earth System Model (CESM) for planetary and exoplanetary applications. The system files, source code, initial conditions files, and namelists provided do not run standalone. ExoCAM is a patch to be used with standard distributions of CESM version 1.2.1 (http://www.cesm.ucar.edu/models/current.html), and is also intended to be run with ExoRT (ascl:2002.019), a correlated-k radiative transfer package.

Exo-Transmit calculates the transmission spectrum of an exoplanet atmosphere given specified input information about the planetary and stellar radii, the planet's surface gravity, the atmospheric temperature-pressure (T-P) profile, the location (in terms of pressure) of any cloud layers, the composition of the atmosphere, and opacity data for the atoms and molecules that make up the atmosphere. The code solves the equation of radiative transfer for absorption of starlight passing through the planet's atmosphere as it transits, accounting for the oblique path of light through the planetary atmosphere along an Earth-bound observer's line of sight. The fraction of light absorbed (or blocked) by the planet plus its atmosphere is calculated as a function of wavelength to produce the wavelength-dependent transmission spectrum. Functionality is provided to simulate the presence of atmospheric aerosols in two ways: an optically thick (gray) cloud deck can be generated at a user-specified height in the atmosphere, and the nominal Rayleigh scattering can be increased by a specified factor.

EXO-NAILER (EXOplanet traNsits and rAdIal veLocity fittER) efficiently fits exoplanet transit lightcurves, radial velocities (RVs) or both. The code handles data taken with different instruments. For RVs, a different center-of-mass velocity can be fitted for each instrument to account for offsets between them; if jitter is included, a different jitter term can also fitted for each instrument. For transits, a different photometric jitter can be fitted to each instrument as can different limb-darkening coefficients and different transit depths. In addition to general options that need to be set, EXO-NAILER also requires that photometry and radial velocity options be defined for each instrument.

The heterogeneity of papers dealing with the discovery and characterization of exoplanets makes every attempt to maintain a uniform exoplanet catalog almost impossible. Four sources currently available online (NASA Exoplanet Archive, Exoplanet Orbit Database, Exoplanet Encyclopaedia, and Open Exoplanet Catalogue) are commonly used by the community, but they can hardly be compared, due to discrepancies in notations and selection criteria.
Exo-MerCat is a Python code that collects and selects the most precise measurement for all interesting planetary and orbital parameters contained in the four databases, accounting for the presence of multiple aliases for the same target. It can download information about the host star as well by the use of Virtual Observatory ConeSearch connections to the major archives such as SIMBAD and those available in VizieR. A Graphical User Interface is provided to filter data based on the user's constraints and generate automatic plots that are commonly used in the exoplanetary community.
With Exo-MerCat, we retrieved a unique catalog that merges information from the four main databases, standardizing the output and handling notation differences issues. Exo-MerCat can correct as many issues that prevent a direct correspondence between multiple items in the four databases as possible, with the available data. The catalog is available as a VO resource for everyone to use and it is periodically updated, according to the update rates of the source catalogs.

The Exoplanet Detection Map Calculator (Exo-DMC) performs statistical analysis of exoplanet surveys results using Monte Carlo methods. Written in Python, it is the latest rendition of the MESS (Multi-purpose Exoplanet Simulation System, ascl:1111.009). Exo-DMC combines the information on the target stars with instrument detection limits to estimate the probability of detection of companions within a user defined range of masses and physical separations, ultimately generating detection probability maps. The software allows for a high level of flexibility in terms of possible assumptions on the synthetic planet population to be used for the determination of the detection probability.

The EXtraction of COsmological Parameters software (EXCOP) is a set of C and IDL programs together with a very large database of cosmological models generated by CMBFAST (ascl:9909.004) that will compute likelihood functions for cosmological parameters given some CMB data. This is the software and database used in the Stompor et al. (2001) analysis of a high resoultion Maxima1 CMB anisotropy map.

EXCEED-DM (EXtended Calculation of Electronic Excitations for Direct detection of Dark Matter) provides a complete framework for computing DM-electron interaction rates. Given an electronic configuration, EXCEED-DM computes the relevant electronic matrix elements, then particle physics specific rates from these matrix elements. This allows for separation between approximations regarding the electronic state configuration, and the specific calculation being performed.

EVolve calculates the chemical composition and surface pressure of a ID atmosphere on a rocky planet that is being produced by volcanic activity, as it grows over time. Once the initial volatile content of the planet's mantle and the composition and resultant surface pressure of any pre-existing atmosphere is set, the volcanic degassing model EVo (ascl:2307.052) calculates the amount and speciation of any volcanic gases released into the atmosphere over each time step. Atmospheric processing is calculated using FastChem (ascl:1804.025); thermochemical equilibrium is assumed so the final chemical composition of the atmosphere is calculated according to the pre-set surface temperature.