Results 901-1000 of 3361 (3279 ASCL, 82 submitted)
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 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.
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.
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.
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.
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.
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.
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.
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.
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).
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.
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.
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.
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.
exoinformatics computes the entropy of a planetary system's size ordering using three different entropy methods: tally-scores, integral path, and change points.
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.
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.
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.
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 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.
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.
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.
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.
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.
Exorings, written in Python, contains tools for displaying and fitting giant extrasolar planet ring systems; it uses FITS formatted data for input.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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).
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.
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.
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.
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.
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.
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.
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.
EzTao models time series as a continuous-time autoregressive moving-average (CARMA) process. EzTao utilizes celerite (ascl:1709.008), a fast and scalable Gaussian Process Regression library, to evaluate the likelihood function. On average, EzTao is ten times faster than other tools relying on a Kalman filter for likelihood computation.
Light curves from the Kepler telescope rely on "postage stamp" cutouts of a few pixels near each of 200,000 target stars. These light curves are optimized for the detection of short-term signals like planet transits but induce systematics that overwhelm long-term variations in stellar flux. Longer-term effects can be recovered through analysis of the Full Frame Images, a set of calibration data obtained monthly during the Kepler mission. The Python package f3 analyzes the Full Frame Images to infer long-term astrophysical variations in the brightness of Kepler targets, such as magnetic activity or sunspots on slowly rotating stars.
FABADA (Fully Adaptive Bayesian Algorithm for Data Analysis) performs non-parametric noise reduction using Bayesian inference. It iteratively evaluates possible smoothed models of the data to estimate the underlying signal that is statistically compatible with the noisy measurements. Iterations stop based on the evidence E and the χ2 statistic of the last smooth model, and the expected value of the signal is computed as a weighted average of the smooth models. Though FABADA was written for astronomical data, such as spectra (1D) or images (2D), it can be used as a general noise reduction algorithm for any one- or two-dimensional data; the only requisite of the input data is an estimation of its associated variance.
FAC calculates various atomic radiative and collisional processes, including radiative transition rates, collisional excitation and ionization by electron impact, energy levels, photoionization, and autoionization, and their inverse processes radiative recombination and dielectronic capture. The package also includes a collisional radiative model to construct synthetic spectra for plasmas under different physical conditions.
FacetClumps extracts and analyses clumpy structure in molecular clouds. Written in Python and based on the Gaussian Facet model, FacetClumps extracts signal regions using morphology, and segments the signal regions into local regions with a gradient-based method. It then applies a connectivity-based minimum distance clustering method to cluster the local regions to the clump centers. FacetClumps automatically adjusts its parameters to local situations to improve adaptability, and is optimized to detect faint and overlapping clumps.
The Faiss library performs efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning. Faiss is written in C++ with complete wrappers for Python/numpy. Some of the most useful algorithms are implemented on the GPU.
The CASA (1107.013) task FAKEOBS generates model visibilities from already-existing measurement sets. This task can be used to substitute all the visibilities of the target with simulations computed from any model image. The measurement can either be with real or simulated data, the target can have been observed in mosaic mode, and there can be several sources (e.g., bandpass calibrator, flux/phase calibrator, and target).
FALCO (Fast Linearized Coronagraph Optimizer) performs coronagraphic focal plane wavefront correction. It includes routines for pair-wise probing estimation of the complex electric field and Electric Field Conjugation (EFC) control. FALCO utilizes and builds upon PROPER (ascl:1405.006) and rapidly computes the linearized response matrix for each DM, which facilitates re-linearization after each control step for faster DM-integrated coronagraph design and wavefront correction experiments. A Python 3 implementation of FALCO (ascl:2304.005) is also available.
FALCO (Fast Linearized Coronagraph Optimizer) performs coronagraphic focal plane wavefront correction. It includes routines for pair-wise probing estimation of the complex electric field and Electric Field Conjugation (EFC) control. FALCO utilizes and builds upon PROPER (ascl:1405.006) and rapidly computes the linearized response matrix for each DM, which facilitates re-linearization after each control step for faster DM-integrated coronagraph design and wavefront correction experiments. A MATLAB implementation of FALCO (ascl:2304.004) is also available.
FAlCon-DNS (Framework of time schemes for direct numerical simulation of annular convection) solves for 2-D convection in an annulus and analyzes different time integration schemes. The framework contains a suite of IMEX, IMEXRK and RK time integration schemes. The code uses a pseudospectral method for spatial discretization. The governing equations contain both numerically stiff (diffusive) and non-stiff (advective) components for time discretization. The software offers OpenMP for parallelization.
FalconIC generates discrete particle positions, velocities, masses and pressures based on linear Boltzmann solutions that are computed by libraries such as CLASS and CAMB. FalconIC generates these initial conditions for any species included in the selection, including Baryons, Cold Dark Matter and Dark Energy fluids. Any species can be set in Eulerian (on a fixed grid) or Lagrangian (particle motion) representation, depending on the gauge and reality chosen. That is, for relativistic initial conditions in the synchronous comoving gauge, Dark Matter can only be described in an Eulerian representation. For all other choices (Relativistic in Longitudinal gauge, Newtonian with relativistic expansion rates, Newtonian without any notion of radiation), all species can be treated in all representations. The code also computes spectra. FalconIC is useful for comparative studies on initial conditions.
FAMA (Fast Automatic MOOG Analysis), written in Perl, computes the atmospheric parameters and abundances of a large number of stars using measurements of equivalent widths (EWs) automatically and independently of any subjective approach. Based on the widely-used MOOG code, it simultaneously searches for three equilibria, excitation equilibrium, ionization balance, and the relationship between logn(FeI) and the reduced EWs. FAMA also evaluates the statistical errors on individual element abundances and errors due to the uncertainties in the stellar parameters. Convergence criteria are not fixed "a priori" but instead are based on the quality of the spectra.
The FAMED (Fast and AutoMated pEak bagging with Diamonds) pipeline is a multi-platform parallelized software that performs and automates extraction and mode identification of oscillation frequencies for solar-like pulsators. The pipeline can be applied to a large variety of stars, ranging from hot F-type main sequence, up to stars evolving along the red giant branch, settled into the core-Helium-burning main sequence, and even evolved beyond towards the early asymptotic giant branch. FAMED is based on DIAMONDS (ascl:1410.001), a Bayesian parameter estimation and model comparison by means of the nested sampling Monte Carlo (NSMC) algorithm.
FAMIAS (Frequency Analysis and Mode Identification for Asteroseismology) is a package of software tools programmed in C++ for the analysis of photometric and spectroscopic time-series data. FAMIAS provides analysis tools that are required for the steps between the data reduction and the seismic modeling. Two main sets of tools are incorporated in FAMIAS. The first set permits to search for periodicities in the data using Fourier and non-linear least-squares fitting techniques. The other set permits to carry out a mode identification for the detected pulsation frequencies to determine their harmonic degree l, and azimuthal order m. FAMIAS is applicable to main-sequence pulsators hotter than the Sun. This includes Gamma Dor, Delta Sct stars, slowly pulsating B (SPB)-stars and Beta Cep stars - basically all stars for which empirical mode identification is required to successfully carry out asteroseismology.
FARGO is an efficient and simple modification of the standard transport algorithm used in explicit eulerian fixed polar grid codes, aimed at getting rid of the average azimuthal velocity when applying the Courant condition. This results in a much larger timestep than the usual procedure, and it is particularly well-suited to the description of a Keplerian disk where one is traditionally limited by the very demanding Courant condition on the fast orbital motion at the inner boundary. In this modified algorithm, the timestep is limited by the perturbed velocity and by the shear arising from the differential rotation. The speed-up resulting from the use of the FARGO algorithm is problem dependent. In the example presented in the code paper below, which shows the evolution of a Jupiter sized protoplanet embedded in a minimum mass protoplanetary nebula, the FARGO algorithm is about an order of magnitude faster than a traditional transport scheme, with a much smaller numerical diffusivity.
A successor of FARGO (ascl:1102.017), FARGO3D is a versatile HD/MHD code that runs on clusters of CPUs or GPUs, with special emphasis on protoplanetary disks. FARGO3D offers Cartesian, cylindrical or spherical geometry; 1-, 2- or 3-dimensional calculations; and orbital advection (aka FARGO) for HD and MHD calculations. As in FARGO, a simple Runge-Kutta N-body solver may be used to describe the orbital evolution of embedded point-like objects. There is no need to know CUDA; users can develop new functions in C and have them translated to CUDA automatically to run on GPUs.
FASMA delivers the atmospheric stellar parameters (effective temperature, surface gravity, metallicity, microturbulence, macroturbulence, and rotational velocity) based on the spectral synthesis technique. This technique relies on the comparison of synthetic spectra with observations to yield the best-fit parameters under a χ2 minimization process. FASMA also delivers chemical abundances of 13 elements. Written in Python, the code is wrapped around MOOG (ascl:1202.009) which calculates the synthetic spectra. FASMA includes two grids of models in MOOG readable format, Kurucz and marcs, that cover the parameter space for both dwarf and giant stars with metallicity limit of -5.0 dex.
We place functional constraints on the shape of the inflaton potential from the cosmic microwave background through a variant of the generalized slow roll approximation that allows large amplitude, rapidly changing deviations from scale-free conditions. Employing a principal component decomposition of the source function G'~3(V'/V)^2 - 2V''/V and keeping only those measured to better than 10% results in 5 nearly independent Gaussian constraints that maybe used to test any single-field inflationary model where such deviations are expected. The first component implies < 3% variations at the 100 Mpc scale. One component shows a 95% CL preference for deviations around the 300 Mpc scale at the ~10% level but the global significance is reduced considering the 5 components examined. This deviation also requires a change in the cold dark matter density which in a flat LCDM model is disfavored by current supernova and Hubble constant data and can be tested with future polarization or high multipole temperature data. Its impact resembles a local running of the tilt from multipoles 30-800 but is only marginally consistent with a constant running beyond this range. For this analysis, we have implemented a ~40x faster WMAP7 likelihood method which we have made publicly available.
FAST-PT calculates 1-loop corrections to the matter power spectrum in cosmology. The code utilizes Fourier methods combined with analytic expressions to reduce the computation time down to scale as N log N, where N is the number of grid point in the input linear power spectrum. FAST-PT is extremely fast, enabling mode-coupling integral computations fast enough to embed in Monte Carlo Markov Chain parameter estimation.
FAST (Fitting and Assessment of Synthetic Templates) fits stellar population synthesis templates to broadband photometry and/or spectra. FAST is compatible with the photometric redshift code EAzY (ascl:1010.052) when fitting broadband photometry; it uses the photometric redshifts derived by EAzY, and the input files (for examply, photometric catalog and master filter file) are the same. FAST fits spectra in combination with broadband photometric data points or simultaneously fits two components, allowing for an AGN contribution in addition to the host galaxy light. Depending on the input parameters, FAST outputs the best-fit redshift, age, dust content, star formation timescale, metallicity, stellar mass, star formation rate (SFR), and their confidence intervals. Though some of FAST's functions overlap with those of HYPERZ (ascl:1108.010), it differs by fitting fluxes instead of magnitudes, allows the user to completely define the grid of input stellar population parameters and easily input photometric redshifts and their confidence intervals, and calculates calibrated confidence intervals for all parameters. Note that FAST is not a photometric redshift code, though it can be used as one.
Fastcc returns color corrections for different spectra for various Cosmic Microwave Background experiments. Available in both Python and IDL, the script is easy to use when analyzing radio spectra of sources with data from multiple wide-survey CMB experiments in a consistent way across multiple experiments.
FastChem is an equilibrium chemistry code that calculates the chemical composition of the gas phase for given temperatures and pressures. Written in C++, it is based on a semi-analytic approach and is optimized for extremely fast and accurate calculations.
The Fast Chi-Squared Algorithm is a fast, powerful technique for detecting periodicity. It was developed for analyzing variable stars, but is applicable to many of the other applications where the Fast Fourier Transforms (FFTs) or other periodograms (such as Lomb-Scargle) are currently used. The Fast Chi-squared technique takes a data set (e.g. the brightness of a star measured at many different times during a series of observations) and finds the periodic function that has the best frequency and shape (to an arbitrary number of harmonics) to fit the data. Among its advantages are:
FastCSWT performs a directional continuous wavelet transform on the sphere. The transform is based on the construction of the continuous spherical wavelet transform (CSWT) developed by Antoine and Vandergheynst (1999). A fast implementation of the CSWT (based on the fast spherical convolution developed by Wandelt and Gorski 2001) is also provided.
FastDF (Fast Distribution Function) integrates relativistic particles along geodesics in a comoving periodic volume with forces determined by cosmological linear perturbation theory. Its main application is to set up accurate particle realizations of the linear phase-space distribution of massive relic neutrinos by starting with an analytical solution deep in radiation domination. Such particle realizations are useful for Monte Carlo experiments and provide consistent initial conditions for cosmological N-body simulations. Gravitational forces are calculated from three-dimensional potential grids, which are obtained by convolving random phases with linear transfer functions using Fast Fourier Transforms. The equations of motion are solved using a symplectic leapfrog integration scheme to conserve phase-space density and prevent the build-up of errors. Particles can be exported in different gauges and snapshots are provided in the HDF5 format, compatible with N-body codes like SWIFT (ascl:1805.020) and Gadget-4 (ascl:2204.014). The code has an interface with CLASS (ascl:1106.020) for calculating transfer functions and with monofonIC (ascl:2008.024) for setting up initial conditions with dark matter, baryons, and neutrinos.
Because of their simplicity, axisymmetric mass distributions are often used to model gravitational lenses. Since galaxies are usually observed to have elliptical light distributions, mass distributions with elliptical density contours offer more general and realistic lens models. They are difficult to use, however, since previous studies have shown that the deflection angle (and magnification) in this case can only be obtained by rather expensive numerical integrations. We present a family of lens models for which the deflection can be calculated to high relative accuracy (10-5) with a greatly reduced numerical effort, for small and large ellipticity alike. This makes it easier to use these distributions for modeling individual lenses as well as for applications requiring larger computing times, such as statistical lensing studies. FASTELL is a code to calculate quickly and accurately the lensing deflection and magnification matrix for the softened power-law elliptical mass distribution (SPEMD) lens galaxy model. The SPEMD consists of a softened power-law radial distribution with elliptical isodensity contours.
The FastJet package provides fast native implementations of many sequential recombination algorithms, including the longitudinally invariant kt longitudinally invariant inclusive Cambridge/Aachen and anti-kt jet finders. It also provides a uniform interface to external jet finders via a plugin mechanism. FastJet also includes tools for calculating jet areas and performing background (pileup/UE) subtraction and for jet substructure analyses.
The analysis of weak lensing data requires to account for missing data such as masking out of bright stars. To date, the majority of lensing analyses uses the two point-statistics of the cosmic shear field. These can either be studied directly using the two-point correlation function, or in Fourier space, using the power spectrum. The two-point correlation function is unbiased by missing data but its direct calculation will soon become a burden with the exponential growth of astronomical data sets. The power spectrum is fast to estimate but a mask correction should be estimated. Other statistics can be used but these are strongly sensitive to missing data. The solution that is proposed by FASTLens is to properly fill-in the gaps with only NlogN operations, leading to a complete weak lensing mass map from which one can compute straight forwardly and with a very good accuracy any kind of statistics like power spectrum or bispectrum.
PSF fitting photometry allows a simultaneously fit of a PSF profile on the sources. Many routines use PSF fitting photometry, including IRAF/allstar, Strarfinder, and Convphot. These routines are in general complex to use and slow. FASTPHOT is optimized for prior extraction (the position of the sources is known) and is very fast and simple.
FastPM solves the gravity Possion equation with a boosted particle mesh. Arbitrary time steps can be used. The code is intended to study the formation of large scale structure and supports plain PM and Comoving-Lagranian (COLA) solvers. A broadband correction enforces the linear theory model growth factor at large scale. FastPM scales extremely well to hundred thousand MPI ranks, which is possible through the use of the PFFT Fourier Transform library. The size of mesh in FastPM can vary with time, allowing one to use coarse force mesh at high redshift with increase temporal resolution for accurate large scale modes. The code supports a variety of Greens function and differentiation kernels, though for most practical simulations the choice of kernels does not make a difference. A parameter file interpreter is provided to validate and execute the configuration files without running the simulation, allowing creative usages of the configuration files.
FastQSL calculate the squashing factor Q at the photosphere, a cross section, or a box volume, given a 3D magnetic field with Cartesian, uniform or stretched grids. It is available in IDL and in an optimized version using Fortran for calculations and field line tracing. Use of a GPU accelerates a step-size adaptive scheme for the most computationally intensive part, the field line tracing, making the code fast and efficient.
Fastrometry is a Python implementation of the fast world coordinate solution solver for the FITS standard astronomical image. When supplied with the approximate field center (+-25%) and the approximate field scale (+-10%) of the telescope and detector system the astronomical image is from, fastrometry provides WCS solutions almost instantaneously. The algorithm is also originally implemented with parallelism enabled in the Windows FITS image processor and viewer CCDLAB (ascl:2206.021).
fastSHT performs spherical harmonic transforms on a large number of spherical maps. It converts massive SHT operations to a BLAS level 3 problem and uses the highly optimized matrix multiplication toolkit to accelerate the computation. GPU acceleration is supported and can be very effective. The core code is written in Fortran, but a Python wrapper is provided and recommended.
FastSpecFit models the observed-frame optical spectroscopy and broadband photometry of extragalactic targets using physically grounded stellar continuum and emission-line templates. The code handles data from the Dark Energy Spectroscopic Instrument (DESI) Survey, which is amassing spectrophotometry for an unprecedented 40 million extragalactic targets, although the algorithms are general enough to accommodate other upcoming, massively multiplexed spectroscopic surveys. FastSpecFit extracts nearly 800 observed- and rest-frame quantities from each target, including light-weighted ages and stellar velocity dispersions based on the underlying stellar continuum; line-widths, velocity shifts, integrated fluxes, and equivalent widths for nearly 40 rest-frame ultraviolet, optical, and near-infrared emission lines arising from both star formation and active galactic nuclear activity; and K-corrections and rest-frame absolute magnitudes and colors. Moreover, FastSpecFit is designed with speed and parallelism in mind, enabling it to deliver robust model fits to tens of millions of targets.
FAT (Fully Automated TiRiFiC) is an automated procedure that fits tilted-ring models to Hi data cubes of individual, well-resolved galaxies. The method builds on the 3D Tilted Ring Fitting Code (TiRiFiC, ascl:1208.008). FAT accurately models the kinematics and the morphologies of galaxies with an extent of eight beams across the major axis in the inclination range 20°-90° without the need for priors such as disc inclination. FAT's performance allows us to model the gas kinematics of many thousands of well-resolved galaxies, which is essential for future HI surveys, with the Square Kilometre Array and its pathfinders.
FATS facilitates and standardizes feature extraction for time series data; it quickly and efficiently calculates a compilation of many existing light curve features. Users can characterize or analyze an astronomical photometric database, though this library is not necessarily restricted to the astronomical domain and can also be applied to any kind of time series data.
The fragmentation and bulk composition tracking package contains two codes. The fragmentation code models fragmentation in collisions for the C version of REBOUND (ascl:1110.016). This code requires setting two global parameters. It automatically produces a collision report that details the time of every collision, the bodies involved, how the collision was resolved, and how many fragments were produced; collision outcomes are assigned a numerical value. The bulk composition tracking code tracks the composition change as a function of mass exchange for bodies with a homogenous composition. It is a post-processing code that works in conjunction with the fragmentation code, and requires the collision report generated by the fragmentation code.
FBEYE, the "Flares By-Eye" detection suite, is written in IDL and analyzes Kepler light curves and validates flares. It works on any 3-column light curve that contains time, flux, and error. The success of flare identification is highly dependent on the smoothing routine, which may not be suitable for all sources.
FCFC (Fast Correlation Function Calculator) computes correlation functions from pair counts. It supports the isotropic 2-point correlation function, anisotropic 2PCF, 2-D 2PCF, and 2PCF Legendre multipoles, among others. Written in C, FCFC takes advantage of three parallelisms that can be used simultaneously, distributed-memory processes via Message Passing Interface (MPI), shared-memory threads via Open Multi-Processing (OpenMP), and single instruction, multiple data (SIMD).
FCLC (Featureless Classification of Light Curves) software describes the static behavior of a light curve in a probabilistic way. Individual data points are converted to densities and consequently probability density are compared instead of features. This gives rise to an independent classification which can corroborate the usefulness of the selected features.
fcmaker creates astronomical finding charts for Observing Blocks (OBs) on the p2 web server from the European Southern Observatory (ESO). It automates the creation of ESO-compliant finding charts for Service Mode and/or Visitor Mode OBs at the Very Large Telescope (VLT). The design of the fcmaker finding charts, based on an intimate knowledge of VLT observing procedures, is fine-tuned to best support night time operations. As an automated tool, fcmaker also allows observers to independently check visually, for the first time, the observing sequence coded inside an OB. This includes, for example, the signs of telescope and position angle offsets.
The spectral disentangling technique can be applied on a time series of observed spectra of a spectroscopic double-lined binary star (SB2) to determine the parameters of orbit and reconstruct the spectra of component stars, without the use of template spectra. fd3 disentangles the spectra of SB2 stars, capable also of resolving the possible third companion. It performs the separation of spectra in the Fourier space which is faster, but in several respects less versatile than the wavelength-space separation. (Wavelength-space separation is implemented in the twin code CRES.) fd3 is written in C and is designed as a command-line utility for a Unix-like operating system. fd3 is a new version of FDBinary (ascl:1705.011), which is now deprecated.
FDBinary disentangles spectra of SB2 stars. The spectral disentangling technique can be applied on a time series of observed spectra of an SB2 to determine the parameters of orbit and reconstruct the spectra of component stars, without the use of template spectra. The code is written in C and is designed as a command-line utility for a Unix-like operating system. FDBinary uses the Fourier-space approach in separation of composite spectra. This code has been replaced with the newer fd3 (ascl:1705.012).
FDIPS is a finite difference iterative potential-field solver that can generate the 3D potential magnetic field solution based on a magnetogram. It is offered as an alternative to the spherical harmonics approach, as when the number of spherical harmonics is increased, using the raw magnetogram data given on a grid that is uniform in the sine of the latitude coordinate can result in inaccurate and unreliable results, especially in the polar regions close to the Sun. FDIPS is written in Fortran 90 and uses the MPI library for parallel execution.
FDPS provides the necessary functions for efficient parallel execution of particle-based simulations as templates independent of the data structure of particles and the functional form of the interaction. It is used to develop particle-based simulation programs for large-scale distributed-memory parallel supercomputers. FDPS includes templates for domain decomposition, redistribution of particles, and gathering of particle information for interaction calculation. It uses algorithms such as Barnes-Hut tree method for long-range interactions; methods to limit the calculation to neighbor particles are used for short-range interactions. FDPS reduces the time and effort necessary to write a simple, sequential and unoptimized program of O(N^2) calculation cost, and produces compiled programs that will run efficiently on large-scale parallel supercomputers.
feets characterizes and analyzes light-curves from astronomical photometric databases for modelling, classification, data cleaning, outlier detection and data analysis. It uses machine learning algorithms to determine the numerical descriptors that characterize and distinguish the different variability classes of light-curves; these range from basic statistical measures such as the mean or standard deviation to complex time-series characteristics such as the autocorrelation function. The library is not restricted to the astronomical field and could also be applied to any kind of time series. This project is a derivative work of FATS (ascl:1711.017).
FEniCS solves partial differential equations (PDEs) and enables users to quickly translate scientific models into efficient finite element code. With the high-level Python and C++ interfaces to FEniCS, it is easy to get started, but FEniCS offers also powerful capabilities for more experienced programmers. FEniCS runs on a multitude of platforms ranging from laptops to high-performance clusters, and each component of the FEniCS platform has been fundamentally designed for parallel processing. This framework allows for rapid prototyping of finite element formulations and solvers on laptops and workstations, and the same code may then be deployed on large high-performance computers.
Bandpass shifting and the (1+z)5 surface brightness dimming (for a fixed width filter) make standard tools for the extraction of structural parameters of galaxies wavelength dependent. If only few (or one) observed high-res bands exist, this dependence has to be corrected to make unbiased statements on the evolution of structural parameters or on galaxy subsamples defined by morphology. FERENGI artificially redshifts low-redshift galaxy images to different redshifts by applying the correct cosmological corrections for size, surface brightness and bandpass shifting. A set of artificially redshifted galaxies in the range 0.1<z<1.1 using a set of ~100 SDSS low-redshift (v<7000 km s-1) images as input has been created to use as a training set of realistic images of galaxies of diverse morphologies and a large range of redshifts for the GEMS and COSMOS galaxy evolution projects. This training set allows other studies to investigate and quantify the effects of cosmological redshift on the determination of galaxy morphologies, distortions, and other galaxy properties that are potentially sensitive to resolution, surface brightness, and bandpass issues. The data sets are also available for download from the FERENGI website.
fermi-gce-flows uses a machine learning-based technique to characterize the contribution of modeled components, including unresolved point sources, to the GCE. It can perform posterior parameter estimation while accounting for pixel-to-pixel spatial correlations in the gamma-ray map. On application to Fermi data, the method generically attributes a smaller fraction of the GCE flux to unresolved point source-like emission when compared to traditional approaches.
Fermipy facilitates analysis of data from the Large Area Telescope (LAT) with the Fermi Science Tools. It is built on the pyLikelihood interface of the Fermi Science Tools and provides a set of high-level tools for performing common analysis tasks, including data and model preparation with the gt-tools, extracting a spectral energy distribution (SED) of a source, and generating TS and residual maps for a region of interest. Fermipy also finds new source candidates and can localize a source or fit its spatial extension. The package uses a configuration-file driven workflow in which the analysis parameters (data selection, IRFs, and ROI model) are defined in a YAML configuration file. Analysis is executed through a python script that calls the methods of GTAnalysis to perform different analysis operations.
Fermi Science Tools is a suite of tools for the analysis of both the Large-Area Telescope (LAT) and the Gamma-ray Burst Monitor (GBM) data, including point source analysis for generating maps, spectra, and light curves, pulsar timing analysis, and source identification.
FERRE matches physical models to observed data, taking a set of observations and identifying the model parameters that best reproduce the data, in a chi-squared sense. It solves the common problem of having numerical parametric models that are costly to evaluate and need to be used to interpret large data sets. FERRE provides flexibility to search for all model parameters, or hold constant some of them while searching for others. The code is written to be truly N-dimensional and fast. Model predictions are to be given as an array whose values are a function of the model parameters, i.e., numerically. FERRE holds this array in memory, or in a direct-access binary file, and interpolates in it. The code returns, in addition to the optimal set of parameters, their error covariance, and the corresponding model prediction. The code is written in FORTRAN90.
FETCH (Fast Extragalactic Transient Candidate Hunter) provides real-time classification of candidates from single pulse search pipelines. The package takes in a candidate file of frequency-time and DM-time data and, for each candidate and choice of model, provides the probability that the candidate is an FRB. FETCH also provides a framework for fine-tuning the models to further improve its performance for particular backends.
Fewbody is a numerical toolkit for simulating small-N gravitational dynamics. It is a general N-body dynamics code, although it was written for the purpose of performing scattering experiments, and therefore has several features that make it well-suited for this purpose. Fewbody uses the 8th-order Runge-Kutta Prince-Dormand integration method with 9th-order error estimate and adaptive timestep to advance the N-body system forward in time. It integrates the usual formulation of the N-body equations in configuration space, but allows for the option of global pairwise Kustaanheimo-Stiefel (K-S) regularization (Heggie 1974; Mikkola 1985). The code uses a binary tree algorithm to classify the N-body system into a set of independently bound hierarchies, and performs collisions between stars in the “sticky star” approximation. Fewbody contains a collection of command line utilities that can be used to perform individual scattering and N-body interactions, but is more generally a library of functions that can be used from within other codes.
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