COCOA (Cluster simulatiOn Comparison with ObservAtions) creates idealized mock photometric observations using results from numerical simulations of star cluster evolution. COCOA is able to present the output of realistic numerical simulations of star clusters carried out using Monte Carlo or N-body codes in a way that is useful for direct comparison with photometric observations. The code can simulate optical observations from simulation snapshots in which positions and magnitudes of objects are known. The parameters for simulating the observations can be adjusted to mimic telescopes of various sizes. COCOA also has a photometry pipeline that can use standalone versions of DAOPHOT (ascl:1104.011) and ALLSTAR to produce photometric catalogs for all observed stars.

CoCoNuT is a general relativistic hydrodynamics code with dynamical space-time evolution. The main aim of this numerical code is the study of several astrophysical scenarios in which general relativity can play an important role, namely the collapse of rapidly rotating stellar cores and the evolution of isolated neutron stars. The code has two flavors: CoCoA, the axisymmetric (2D) magnetized version, and CoCoNuT, the 3D non-magnetized version.

The COCOPLOT (COlor COllapsed PLOTting) quick-look and context image code conveys spectral profile information from all of the spatial pixels in a 3D datacube as a single image using color. It can also identify and expose temporal behavior and display and highlight solar features. COCOPLOT thus aids in identifying regions of interest quickly. The software is available in Python and IDL, and can be used as a standalone package or integrated into other software.

COFFE (COrrelation Function Full-sky Estimator) computes quantities in linear perturbation theory. It computes the full-sky and flat-sky 2-point correlation function (2PCF) of galaxy number counts, taking into account all of the effects, including density, RSD, and lensing. It also determines the full-sky, flat-sky, and redshift-averaged multipoles of the 2PCF, and the flat-sky Gaussian covariance matrix of the multipoles of the 2PCF.

cola_halo generates hundreds of realizations on the fly. This parallel cosmological N-body simulation code generates random Gaussian initial condition using 2LPTIC (ascl:1201.005), time evolves N-body particles with colacode (ascl:1602.021), and finds dark-matter halos with the Friends-of-Friends code (ascl:2407.012).

COLAcode is a serial particle mesh-based N-body code illustrating the COLA (COmoving Lagrangian Acceleration) method; it solves for Large Scale Structure (LSS) in a frame that is comoving with observers following trajectories calculated in Lagrangian Perturbation Theory (LPT). It differs from standard N-body code by trading accuracy at small-scales to gain computational speed without sacrificing accuracy at large scales. This is useful for generating large ensembles of accurate mock halo catalogs required to study galaxy clustering and weak lensing; such catalogs are needed to perform detailed error analysis for ongoing and future surveys of LSS.

COLASolver creates Particle-Mesh (PM) N-body simulations; the code is fast and very flexible, and can compute a wide range of models. For models with complex dynamics (screened models), it provides several options from doing it exactly to approximate but fast to just simulating linear theory equations. Every time-consuming operation is parallelized over MPI and OpenMP. It uses a slab-based parallelization that works well for fast approximate (COLA) simulations but does not perform as well for high resolution simulations. COLASolver can also be used as an analysis code for results from other simulations.

CoLFI (Cosmological Likelihood-Free Inference) estimates parameters directly from the observational data sets using neural density estimators (NDEs); it is a fully ANN-based framework that differs from the Bayesian inference. The package contains three NDEs that are used to estimate parameters: an artificial neural network (ANN), a mixture density network (MDN), and a mixture neural network (MNN). CoLFI can learn the conditional probability density using samples generated by models, and the posterior distribution can be obtained for given observational data.

COLIBRÌ (which roughly stands for “Cosmological Libraries”) computes cosmological quantities such as ages, distances, power spectra, and correlation functions. It supports Lambda-CDM cosmologies plus extensions including massive neutrinos, non-flat geometries, evolving dark energy (w0-wa) models, and numerical recipes for f(R) gravity. COLIBRÌ is built especially for large-scale structure purposes and can interact with the Boltzmann solvers CAMB (ascl:1102.026) and CLASS (ascl:1106.020).

collapse calculates the spherical−collapse for standard cosmological models as well as for dark energy models when the dark energy can be taken to be spatially homogeneous. The calculation is valid on sub−horizon scales and takes a top−hat perturbation to exist in an otherwise featureless cosmos and follows its evolution into the non−linear regime where it reaches a maximum size and then recollapses. collapse provides the user with the linear−collapse threshold (delta_c) and the virial overdensity (Delta_v) for the collapsed halo over a range of cosmic scale factors.

CoLoRe (Cosmological Lofty Realization) generates fast mock realizations of a given galaxy sample using a lognormal model or LPT for the matter density. Tt can simulate a variety of cosmological tracers, including photometric and spectroscopic galaxies, weak lensing, and intensity mapping. CoLoRe is a parallel C code, and its behavior is controlled primarily by the input param file.

ColorPro automatically obtains robust colors across images of varied PSF. To correct for the flux lost in images with poorer PSF, the "detection image" is blurred to match the PSF of these other images, allowing observation of how much flux is lost. All photometry is performed in the highest resolution frame (images being aligned given WCS information in the FITS headers), and identical apertures are used in every image. Usually isophotal apertures are used, as determined by SExtractor (ascl:1010.064). Using SExSeg (ascl:1508.006), object aperture definitions can be pre-defined and object detections from different image filters can be combined automatically into a single comprehensive "segmentation map." After producing the final photometric catalog, ColorPro can automatically run BPZ (ascl:1108.011) to obtain Bayesian Photometric Redshifts.

Colossus is a collection of Python modules for cosmology and dark matter halos calculations. It performs cosmological calculations with an emphasis on structure formation applications, implements general and specific density profiles, and provides a large range of models for the concentration-mass relation, including a conversion to arbitrary mass definitions.

COLT (Cosmic Lyman-alpha Transfer) is a Monte Carlo radiative transfer (MCRT) solver for post-processing hydrodynamical simulations on arbitrary grids. These include a plane parallel slabs, spherical geometry, 3D Cartesian grids, adaptive resolution octrees, unstructured Voronoi tessellations, and secondary outputs. COLT also includes several visualization and analysis tools that exploit the underlying ray-tracing algorithms or otherwise benefit from an efficient hybrid MPI + OpenMP parallelization strategy within a flexible C++ framework.

COMB supports the simulation on the sphere of compact objects embedded in a stochastic background process of specified power spectrum. Support is provided to add additional white noise and convolve with beam functions. Functionality to support functions defined on the sphere is provided by the S2 code (ascl:1606.008); HEALPix (ascl:1107.018) and CFITSIO (ascl:1010.001) are also required.

comb is a single-dish radio astronomy spectral line data reduction and analysis package developed at AT&T Bell labs and was used for data reduction for many single-dish telescopes, including Bell Labs 7-m, NRAO 12-m, DSN network, FCRAO 14-m, Arecibo, AST/RO, SEST, BIMA, and in 2011-2012, the Stratospheric Terahertz Observatory. A cookbook for the code is available.

ComEst calculates the completeness of CCD images conducted in astronomical observations saved in the FITS format. It estimates the completeness of the source finder SExtractor (ascl:1010.064) on the optical and near-infrared (NIR) imaging of point sources or galaxies as a function of flux (or magnitude) directly from the image itself. It uses PyFITS (ascl:1207.009) and GalSim (ascl:1402.009) to perform the end-to-end estimation of the completeness and can also estimate the purity of the source detection.

COMET (Clustering Observables Modelled by Emulated perturbation Theory) provides emulated predictions of large-scale structure observables from models that are based on perturbation theory. It substantially speeds up these analytic computations without any relevant sacrifice in accuracy, enabling an extremely efficient exploration of large-scale structure likelihoods. At its core, COMET exploits an evolution mapping approach which gives it a high degree of flexibility and allows it to cover a wide cosmology parameter space at continuous redshifts up to z∼3z \sim 3z∼3. Among others, COMET supports parameters for cold dark matter density (ωc\omega_cωc​), baryon density (ωb\omega_bωb​), Scalar spectral index (nsn_sns​), Hubble expansion rate (hhh) and Curvature density (ΩK\Omega_KΩK​). The code can obtain the real-space galaxy power spectrum at one-loop order multipoles (monopole, quadrupole, hexadecapole) of the redshift-space, power spectrum at one-loop order, the linear matter power spectrum (with and without infrared resummation), Gaussian covariance matrices for the real-space power spectrum, and redshift-space multipoles and χ2\chi^2χ2's for arbitrary combinations of multipoles. COMET provides an easy-to-use interface for all of these computations.

Comet is a Python implementation of the VOEvent Transport Protocol (VTP). VOEvent is the IVOA system for describing transient celestial events. Details of transients detected by many projects, including Fermi, Swift, and the Catalina Sky Survey, are currently made available as VOEvents, which is also the standard alert format by future facilities such as LSST and SKA. The core of Comet is a multifunction VOEvent broker, capable of receiving events either by subscribing to one or more remote brokers or by direct connection from authors; it can then both process those events locally and forward them to its own subscribers. In addition, Comet provides a tool for publishing VOEvents to the global VOEvent backbone.

Commander 2 is a Gibbs sampling code for joint CMB estimation and component separation. The Commander framework uses a parametrized physical model of the sky to perform statistically-rigorous analyses of multi-frequency, multi-resolution CMB data on the full and partial (flat) sky, as well as cross-correlation analyses with large-scale structure datasets.

CoMover determines the probability that two stars are co-moving and thus gravitationally bound. It uses the sky position, proper motion, parallax and optionally the heliocentric radial velocity of a host star (with their respective measurement errors), and compares it to the observables of a potential companion (with their respective measurement errors). The sky position and proper motion of the potential companion star are required, and its heliocentric radial velocity and parallax are facultative inputs to refine its co-moving probability.

If all kinematic observables of the host star are provided, a single spatial-kinematic model is built, consisting of a single 6-dimensional multivariate Gaussian in Galactic coordinates (XYZ) and space velocities (UVW). The observables of the potential companion are then compared to this model and a given field-stars model with Bayes' theorem by marginalizing over any missing kinematic observables of the companion star with analytical integral solutions. The field stars are modeled using a 10-component multivariate Gaussian, accurate for stars within a few hundred parsecs of the Sun. In the case where a heliocentric radial velocity is missing for the host star, the single host-star multivariate Gaussian model is replaced with a series of host star models and numerically marginalized over by taking the numerical sum of the host-star model probabilities.

The software used to transform the tabular USNO/AE98 asteroid ephemerides into a Chebyshev polynomial representations, and evaluate them at an arbitrary time is available. The USNO/AE98 consisted of the ephemerides of fifteen of the largest asteroids, and were used in The Astronomical Almanac from 2000 through 2015. These ephemerides are outdated and no longer available, but the software used to store and evaluate them is still available and provides a robust method for storing compact ephemerides of solar system bodies.

The object of the software is to provide a compact binary representation of solar system bodies with eccentric orbits, which can produce the body's position and velocity at an arbitrary instant within the ephemeris' time span. It uses a modification of the Newhall (1989) algorithm to achieve this objective. The Newhall algorithm is used to store both the Jet Propulsion Laboratory DE and the Institut de mécanique céleste et de calcul des éphémérides INPOP high accuracy planetary ephemerides. The Newhall algorithm breaks an ephemeris into a number time contiguous segments, and each segment is stored as a set of Chebyshev polynomial coefficients. The length of the time segments and the maximum degree Chebyshev polynomial coefficient is fixed for each body. This works well for bodies with small eccentricities, but it becomes inefficient for a body in a highly eccentric orbit. The time segment length and maximum order Chebyshev polynomial coefficient must be chosen to accommodate the strong curvature and fast motion near pericenter, while the body spends most of its time either moving slowly near apocenter or in the lower curvature mid-anomaly portions of its orbit. The solution is to vary the time segment length and maximum degree Chebyshev polynomial coefficient with the body's position. The portion of the software that converts tabular ephemerides into a Chebyshev polynomial representation (CPR) performs this compaction automatically, and the portion that evaluates that representation requires only a modest increase in the evaluation time.

The software also allows the user to choose the required tolerance of the CPR. Thus, if less accuracy is required a more compact, somewhat quicker to evaluate CPR can be manufactured and evaluated. Numerical tests show that a fractional precision of 4e-16 may be achieved, only a factor of 4 greater than the 1e-16 precision of a 64-bit IEEE (2019) compliant floating point number.

The software is written in C and designed to work with the C edition of the Naval Observatory Vector Astrometry Software (NOVAS). The programs may be used to convert tabular ephemerides of other solar system bodies as well. The included READ.ME file provides the details of the software and how to use it.

REFERENCES

IEEE Computer Society 2019, IEEE Standard for Floating-Point Arithmetic. IEEE STD 754-2019, IEEE, pp. 1–84

Newhall, X X 1989, 'Numerical Representation of Planetary Ephemerides,' Celest. Mech., 45, 305 - 310

Companion-Finder looks for planets and binary companions in time series spectra by searching for the spectral lines of stellar companions to other stars observed with high-precision radial-velocity surveys.

COMPAS (Compact Object Mergers: Population Astrophysics & Statistics) draws properties for a binary star system from a set of initial distributions and evolves it from zero-age main sequence to the end of its life as two compact remnants. Evolution prescriptions and model parameters are easily adjustable in the software. COMPAS has been used for inference from observations of gravitational-wave mergers, Galactic neutron stars, X-ray binaries, and luminous red novae.

The CompressedFisher library tests whether Fisher forecasts using simulated components are converged. The library contains tools to compute standard Fisher estimates, estimate the level of bias due to the finite number of simulations, and compute the compressed Fisher information. Typical usage of CompressedFisher requires two ensembles of simulations: one set of simulations is given at the fiducial parameters (𝜃) to estimate the covariance matrix. The second is a set of simulated derivatives; these can either be in the form of realizations of the derivatives themselves or simulations evaluate at a set of point in the neighborhood of the fiducial point that the code can use to estimate the derivatives.

ComputePk computes the power spectrum in cosmological simulations. It is MPI parallel and has been tested up to a 4096^3 mesh. It uses the FFTW library. It can read Gadget-3 and GOTPM outputs, and computes the dark matter component. The user may choose between NGP, CIC, and TSC for the mass assignment scheme.

CONCEPT (COsmological N-body CodE in PyThon) simulates cosmological structure formation. It can simulate matter particles evolving under self-gravity in an expanding background. The code offers multiple gravitational solvers and has adaptive time integration built in. In addition to particles, CONCEPT also evolves fluids at various levels of non-linearity, providing the means for the inclusion of more exotic species such as massive neutrinos, as well as for simulations consistent with general relativistic perturbation theory. Various non-standard species, such as decaying cold dark matter, are fully supported. CONCEPT includes a sophisticated initial condition generator and can output snapshots, power spectra, bispectra ,and several kinds of renders.

CONDUCT calculates all components of kinetic tensors in fully ionized electron-ion plasmas at arbitrary magnetic field. It employs a thermal averaging with the Fermi distribution function and can be used when electrons are partially degenerate; it provides, along with the electrical and thermal conductivities, also thermopower (thermoelectric coefficient). CONDUCT takes into account collisions of the electrons with ions and (in solid phase) charged impurities as well as quantum effects on ionic motion in the solid phase. The code's outputs are the longitudinal, transverse, and off-diagonal (Hall) components of electrical and thermal conductivity tensors as well as the components of thermoelectric tensor.

ConeRot extracts velocity perturbations in protoplanetary disks from observed line centroids maps ν∘, by creating axially-symmetric centroid maps. It also derives 3D rotation curves in disk-centered cylindrical coordinates, and can estimate the disk orientation based on line data alone. It approximates the unit opacity surface of an axially symmetric disc by a series of cones whose orientations are fit to the observed velocity centroid in concentric radial domains, or regions, with the disc orientation and the rotation curve both optimized to fit ν∘ in each region. ConeRot extracts the perturbations directly from observations without strong assumptions about the underlying disk model and employs a reduced number of free parameters.

Coniferest is a Python package designed for implementing anomaly detection algorithms and interactive active learning tools. The centerpiece of the package is an Isolation Forest algorithm, known for its superior scoring performance and multi-threading evaluation. This robust anomaly detection algorithm operates by constructing random decision trees.

In addition to the Isolation Forest algorithm, Coniferest also offers two modified versions for active learning: AAD Forest and Pineforest. The AAD Forest modifies the Isolation Forest by reweighting its leaves based on responses from human experts, providing a faster alternative to the ad_examples package.

On the other hand, Pineforest, developed by the SNAD team, employs a filtering algorithm that builds and dismantles trees with each new human-machine iteration step.

Coniferest provides a user-friendly interface for conducting interactive human-machine sessions, facilitating the use of these active anomaly detection algorithms. The SNAD team maintains and utilizes this package primarily for anomaly detection in the field of astronomy, with a particular focus on light-curve data from large time-domain surveys.

connect (COsmological Neural Network Emulator of CLASS using TensorFlow) emulates cosmological parameters using neural networks. This includes both sampling of training data and training of the actual networks using the TensorFlow library. connect aids in cosmological parameter inference by immensely speeding up the process, which is achieved by substituting the cosmological Einstein-Boltzmann solver codes, needed for every evaluation of the likelihood, with a neural network with a 10² to 10³ times faster evaluation time. The code requires CLASS (ascl:1106.020) and Monte Python (ascl:1307.002) if iterative sampling is used.

Consistent Trees generates merger trees and halo catalogs which explicitly ensure consistency of halo properties (mass, position, velocity, radius) across timesteps. It has demonstrated the ability to improve both the completeness (through detecting and inserting otherwise missing halos) and purity (through detecting and removing spurious objects) of both merger trees and halo catalogs. Consistent Trees is able to robustly measure the self-consistency of halo finders and to directly measure the uncertainties in halo positions, halo velocities, and the halo mass function for a given halo finder based on consistency between snapshots in cosmological simulations.

This program addresses the question of what resources are needed to produce a continuous data record of the entire sky down to a given limiting visual magnitude. Toward this end, the program simulates a small camera/telescope or group of small camera/telescopes collecting light from a large portion of the sky. From a given stellar density derived from a Bahcall - Soneira Galaxy model, the program first converts star densities at visual magnitudes between 5 and 20 to number of sky pixels needed to monitor each star simultaneously. From pixels, the program converts input CCD parameters to needed telescope attributes, needed data storage space, and the length of time needed to accumulate data of photometric quality for stars of each limiting visual magnitude over the whole sky. The program steps though photometric integrations one second at a time and includes the contribution from a bright background, read noise, dark current, and atmospheric absorption.

contaminante helps find the contaminant transiting source in NASA's Kepler, K2 or TESS data. When hunting for transiting planets, sometimes signals come from neighboring contaminants. This package helps users identify where the transiting signal comes from in their data. The code uses pixel level modeling of the TargetPixelFile data from NASA's astrophysics missions that are processed with the Kepler pipeline. The output of contaminante is a Python dictionary containing the source location and transit depth, and a contaminant location and depth. It can also output a figure showing where the main target is centered in all available TPFs, what the phase curve looks like for the main target, where the transiting source is centered in all available TPFs, if a transiting source is located outside the main target, or the transiting source phase curve, if a transiting source is located outside the main target.

Contbin bins X-ray data using contours on an adaptively smoothed map. The generated bins closely follow the surface brightness, and are ideal where the surface brightness distribution is not smooth, or the spectral properties are expected to follow surface brightness. Color maps can be used instead of surface brightness maps.

CONTROL (CUTE autONomous daTa ReductiOn pipeLine) produces science-quality output with a single command line with zero user interference for CUTE (Colorado Ultraviolet Transit Experiment) data. It can be used for any single order spectral data in any wavelength without any modification. The pipeline is governed by a parameter file, which is available with this distribution. CONTROL is fully automated and works in a series of steps following standard CCD reduction techniques. It creates a reduction log to track processes carried out and any parameters used.

The IDL package convolve_image.pro transforms images between different instrumental point spread functions (PSFs). It can load an image file and corresponding kernel and return the convolved image, thus preserving the colors of the astronomical sources. Convolution kernels are available for images from Spitzer (IRAC MIPS), Herschel (PACS SPIRE), GALEX (FUV NUV), WISE (W1 - W4), Optical PSFs (multi- Gaussian and Moffat functions), and Gaussian PSFs; they allow the study of the Spectral Energy Distribution (SED) of extended objects and preserve the characteristic SED in each pixel.

ConvPhot measures colors between two images having different resolutions. ConvPhot is designed to work especially for faint galaxies, accurately measuring colors in relatively crowded fields. It makes full use of the spatial and morphological information contained in the highest quality images to analyze multiwavelength data with inhomogeneous image quality.
Written in 2007, ConvPhot has been superseded by T-PHOT (ascl:1609.001)

COpops computes semi-analytically the CO flux of a disc (given initial conditions and age) under the assumption of LTE and optically thick emission. It then runs disc population synthesis using observationally-informed initial conditions. CO fluxes is one of the most easily accessible observables for studying disc evolution; COpops is a faster alternative to running computationally-expensive thermochemical models for hundreds of discs and is accurate, recovering agreement within a factor of three.

Copter is a software package for doing calculations in cosmological perturbation theory. Specifically, Copter includes code for computing statistical observables in the large-scale structure of matter using various forms of perturbation theory, including linear theory, standard perturbation theory, renormalized perturbation theory, and many others. Copter is written in C++ and makes use of the Boost C++ library headers.

CORA analyzes emission line spectra with low count numbers and fits them to a line using the maximum likelihood technique. CORA uses a rigorous application of Poisson statistics. From the assumption of Poissonian noise, the software derives the probability for a model of the emission line spectrum to represent the measured spectrum. The likelihood function is used as a criterion for optimizing the parameters of the theoretical spectrum and a fixed point equation is derived allowing an efficient way to obtain line fluxes. CORA has been applied to an X-ray spectrum with the Low Energy Transmission Grating Spectrometer (LETGS) on board the Chandra observatory.

CORBITS (Computed Occurrence of Revolving Bodies for the Investigation of Transiting Systems) computes the probability that any particular group of exoplanets can be observed to transit from a collection of conjectured exoplanets orbiting a star. The efficient, semi-analytical code computes the areas bounded by circular curves on the surface of a sphere by applying elementary differential geometry. CORBITS is faster than previous algorithms, based on comparisons with Monte Carlo simulations, and tests show that it is extremely accurate even for highly eccentric planets.

CoREAS is a Monte Carlo code for the simulation of radio emission from extensive air showers; it is an update of and successor code to REAS3 (ascl:1107.009). It implements the endpoint formalism for the calculation of electromagnetic radiation directly in CORSIKA (ascl:1202.006). As such, it is parameter-free, makes no assumptions on the emission mechanism for the radio signals, and takes into account the complete complexity of the electron and positron distributions as simulated by CORSIKA.

corner.py uses matplotlib to visualize multidimensional samples using a scatterplot matrix. In these visualizations, each one- and two-dimensional projection of the sample is plotted to reveal covariances. corner.py was originally conceived to display the results of Markov Chain Monte Carlo simulations and the defaults are chosen with this application in mind but it can be used for displaying many qualitatively different samples. An earlier version of corner.py was known as triangle.py.

coronagraph_noise simulates coronagraph noise. Written in IDL, the code includes a generalized coronagraph routine and simulators for the WFIRST Shaped Pupil Coronagraph in both spectroscopy and imaging modes. Functions available include stellar and planetary flux functions, planet photon and zodiacal light count rates, planet-star flux ratio, and clock induced charge count rate, among others. coronagraph_noise also includes routines to smooth a plot by convolving with a Gaussian profile to convolve a spectrum with a given instrument resolution and to take a spectrum that is specified at high spectral resolution and degrade it to a lower resolution. A Python implementation of coronagraph_noise, coronagraph (ascl:2405.019), is also available.

coronagraph provides a Python noise model for directly imaging exoplanets with a coronagraph-equipped telescope. Based on the original IDL code for this coronagraph model, coronograph_noise (ascl:2405.018), the Python version has been expanded in a few key ways. Most notably, the Telescope, Planet, and Star objects used for reflected light coronagraph noise modeling can now be used for transmission and emission spectroscopy noise modeling, making this model a general purpose exoplanet noise model for many different types of observations.

correlcalc calculates two-point correlation function (2pCF) of galaxies/quasars using redshift surveys. It can be used for any assumed geometry or Cosmology model. Using BallTree algorithms to reduce the computational effort for large datasets, it is a parallelised code suitable for running on clusters as well as personal computers. It takes redshift (z), Right Ascension (RA) and Declination (DEC) data of galaxies and random catalogs as inputs in form of ascii or fits files. If random catalog is not provided, it generates one of desired size based on the input redshift distribution and mangle polygon file (in .ply format) describing the survey geometry. It also calculates different realisations of (3D) anisotropic 2pCF. Optionally it makes healpix maps of the survey providing visualization.

CORRFIT is a set of routines that use the cross-correlation method to extract parameters of the line-of-sight velocity distribution from galactic spectra and stellar templates observed on the same system. It works best when the broadening function is well sampled at the spectral resolution used (e.g. 200 km/s dispersion at 2 Angstrom resolution). Results become increasingly sensitive to the spectral match between galaxy and template if the broadening function is not well sampled. CORRFIT does not work well for dispersions less than the velocity sampling interval ('delta' in the code) unless the template is perfect.

Corrfunc is a suite of high-performance clustering routines. The code can compute a variety of spatial correlation functions on Cartesian geometry as well Landy-Szalay calculations for spatial and angular correlation functions on a spherical geometry and is useful for, for example, exploring the galaxy-halo connection. The code is written in C and can be used on the command-line, through the supplied python extensions, or the C API.

CORSIKA (COsmic Ray Simulations for KAscade) is a program for detailed simulation of extensive air showers initiated by high energy cosmic ray particles. Protons, light nuclei up to iron, photons, and many other particles may be treated as primaries. The particles are tracked through the atmosphere until they undergo reactions with the air nuclei or, in the case of unstable secondaries, decay. The hadronic interactions at high energies may be described by several reaction models. Hadronic interactions at lower energies are described, and in particle decays all decay branches down to the 1% level are taken into account. Options for the generation of Cherenkov radiation and neutrinos exist. CORSIKA may be used up to and beyond the highest energies of 100 EeV.