Results 2901-2950 of 3560 (3466 ASCL, 94 submitted)

[ascl:1412.013]
CRPropa: Numerical tool for the propagation of UHE cosmic rays, gamma-rays and neutrinos

CRPropa computes the observable properties of UHECRs and their secondaries in a variety of models for the sources and propagation of these particles. CRPropa takes into account interactions and deflections of primary UHECRs as well as propagation of secondary electromagnetic cascades and neutrinos. CRPropa makes use of the public code SOPHIA (ascl:1412.014), and the TinyXML, CFITSIO (ascl:1010.001), and CLHEP libraries. A major advantage of CRPropa is its modularity, which allows users to implement their own modules adapted to specific UHECR propagation models. An updated version, CRPropa3 (ascl:2208.016), is available.

[submitted]
CRPropa 3.2

Alves Batista, Rafael; Becker Tjus, Julia; Dörner, Julien; Dundovic, Andrej; Eichmann, Björn; Frie, Antonius; Heiter, Christopher; Hoerbe, Mario R.; Kampert, Karl-Heinz; Merten, Lukas; Müller, Gero; Reichherzer, Patrick; Saveliev, Andrey; Schlegel, Leander; Sigl, Günter; van Vliet, Arjen; Winchen, Tobias

The landscape of high- and ultra-high-energy astrophysics has changed in the last decade, largely due to the inflow of data collected by large-scale cosmic-ray, gamma-ray, and neutrino observatories. At the dawn of the multimessenger era, the interpretation of these observations within a consistent framework is important to elucidate the open questions in this field. CRPropa 3.2 is a Monte Carlo code for simulating the propagation of high-energy particles in the Universe. This version represents a major leap forward, significantly expanding the simulation framework and opening up the possibility for many more astrophysical applications. This includes, among others: efficient simulation of high-energy particles in diffusion-dominated domains, self-consistent and fast modelling of electromagnetic cascades with an extended set of channels for photon production, and studies of cosmic-ray diffusion tensors based on updated coherent and turbulent magnetic-field models. Furthermore, several technical updates and improvements are introduced with the new version, such as: enhanced interpolation, targeted emission of sources, and a new propagation algorithm (Boris push). The detailed description of all novel features is accompanied by a discussion and a selected number of example applications.

[ascl:2106.004]
crowdsource: Crowded field photometry pipeline

crowdsource removes a rough sky (the median), find the brighter peaks and fits these sources, computes centroids, and then computes an improved PSF. With this model of the image, the code then iteratively subtracts it and recomputes the median to get a better sky estimate, finds fainter peaks, and calculates a better PSF. crowdsource performs at least four iterations, evaluates the results, and continues until certain thresholds are met. Once the iterative passes are complete, it makes one last pass. If no sources are detected and positions do not vary, it performs photometry for the existing list of stellar positions.

[ascl:1110.020]
CROSS_CMBFAST: ISW-correlation Code

This code is an extension of CMBFAST4.5.1 to compute the ISW-correlation power spectrum and the 2-point angular ISW-correlation function for a given galaxy window function. It includes dark energy models specified by a constant equation of state (w) or a linear parameterization in the scale factor (w0,wa) and a constant sound speed (c2de). The ISW computation is limited to flat geometry. Differently from the original CMBFAST4.5 version dark energy perturbations are implemented for a general dark energy fluid specified by w(z) and c2de in synchronous gauge. For time varying dark energy models it is suggested not to cross the w=-1 line, as Dr. Wenkman says: "never cross the streams", bad things can happen.

[ascl:1708.003]
CRISPRED: CRISP imaging spectropolarimeter data reduction pipeline

CRISPRED reduces data from the CRISP imaging spectropolarimeter at the Swedish 1 m Solar Telescope (SST). It performs fitting routines, corrects optical aberrations from atmospheric turbulence as well as from the optics, and compensates for inter-camera misalignments, field-dependent and time-varying instrumental polarization, and spatial variation in the detector gain and in the zero level offset (bias). It has an object-oriented IDL structure with computationally demanding routines performed in C subprograms called as dynamically loadable modules (DLMs).

[ascl:2103.017]
CRIME: Cosmological Realizations for Intensity Mapping Experiments

CRIME (Cosmological Realizations for Intensity Mapping Experiments) generates mock realizations of intensity mapping observations of the neutral hydrogen distribution. It contains three separate tools, GetHI, ForGet, and JoinT. GetHI generates realizations of the temperature fluctuations due to the 21cm emission of neutral hydrogen. Optionally it can also generate a realization of the point-source continuum emission (for a given population) by sampling the same density distribution, though using this feature greatly affects performance. ForGet generates realizations of the different galactic and extra-galactic foregrounds relevant for intensity mapping experiments using some external datasets (e.g. the Haslam 408 MHz map) stored in the "data"folder. JoinT is provided for convenience; it joins the temperature maps generated by GetHI and ForGet and includes several instrument-dependent effects (in an overly simplistic way).

[ascl:1612.009]
CRETE: Comet RadiativE Transfer and Excitation

CRETE (Comet RadiativE Transfer and Excitation) is a one-dimensional water excitation and radiation transfer code for sub-millimeter wavelengths based on the RATRAN code (ascl:0008.002). The code considers rotational transitions of water molecules given a Haser spherically symmetric distribution for the cometary coma and produces FITS image cubes that can be analyzed with tools like MIRIAD (ascl:1106.007). In addition to collisional processes to excite water molecules, the effect of infrared radiation from the Sun is approximated by effective pumping rates for the rotational levels in the ground vibrational state.

[ascl:1308.009]
CReSyPS: Stellar population synthesis code

CReSyPS (Code Rennais de Synthèse de Populations Stellaires) is a stellar population synthesis code that determines core overshooting amount for Magellanic clouds main sequence stars.

[ascl:1111.002]
CRBLASTER: A Parallel-Processing Computational Framework for Embarrassingly-Parallel Image-Analysis Algorithms

The development of parallel-processing image-analysis codes is generally a challenging task that requires complicated choreography of interprocessor communications. If, however, the image-analysis algorithm is embarrassingly parallel, then the development of a parallel-processing implementation of that algorithm can be a much easier task to accomplish because, by definition, there is little need for communication between the compute processes. I describe the design, implementation, and performance of a parallel-processing image-analysis application, called CRBLASTER, which does cosmic-ray rejection of CCD (charge-coupled device) images using the embarrassingly-parallel L.A.COSMIC algorithm. CRBLASTER is written in C using the high-performance computing industry standard Message Passing Interface (MPI) library. The code has been designed to be used by research scientists who are familiar with C as a parallel-processing computational framework that enables the easy development of parallel-processing image-analysis programs based on embarrassingly-parallel algorithms. The CRBLASTER source code is freely available at the official application website at the National Optical Astronomy Observatory. Removing cosmic rays from a single 800x800 pixel Hubble Space Telescope WFPC2 image takes 44 seconds with the IRAF script lacos_im.cl running on a single core of an Apple Mac Pro computer with two 2.8-GHz quad-core Intel Xeon processors. CRBLASTER is 7.4 times faster processing the same image on a single core on the same machine. Processing the same image with CRBLASTER simultaneously on all 8 cores of the same machine takes 0.875 seconds -- which is a speedup factor of 50.3 times faster than the IRAF script. A detailed analysis is presented of the performance of CRBLASTER using between 1 and 57 processors on a low-power Tilera 700-MHz 64-core TILE64 processor.

[ascl:2206.009]
Craterstats3: Analyze and plot crater count data for planetary surface dating

Craterstats3 analyzes and plots crater count data for planetary surface dating. It is a Python implementation of Craterstats2 (ascl:2206.008) and is designed to replicate the output of the previous version as closely as possible. As before, it produces plots in cumulative, differential, Hartmann, and R-plot styles with possible overlays of crater counts, isochrons, equilibrium functions and epoch boundaries, as well aschronology and impact rate functions. Data can be shown with various binnings or unbinned, and age estimates made by either cumulative fitting, differential fitting, or Poisson timing evaluation. Numerical results can be output as text for further processing elsewhere. A number of published chronology systems are already set up for use, but new ones may be added by the user. The software is designed to be easily integrated into other software, which could allow the addition of a graphical interface or the inclusion of some Craterstats functions into a GIS.

[ascl:2206.008]
Craterstats2: Planetary surface dating from crater size-frequency distribution measurements

Craterstats2 plots crater counts and determining surface ages. The software plots isochrons in cumulative, differential, R-plot and Hartmann presentations, and makes isochron fits to both cumulative and differential data. Hartmann-style piecewise production functions may also be used. A Python implementation of the software, Craterstats3, is also available.

[ascl:1101.008]
CRASH: A Block-Adaptive-Mesh Code for Radiative Shock Hydrodynamics

van der Holst, B.; Toth, G.; Sokolov, I. V.; Powell, K. G.; Holloway, J. P.; Myra, E. S.; Stout, Q.; Adams, M. L.; Morel, J. E.; Drake, R. P.

CRASH (Center for Radiative Shock Hydrodynamics) is a block adaptive mesh code for multi-material radiation hydrodynamics. The implementation solves the radiation diffusion model with the gray or multigroup method and uses a flux limited diffusion approximation to recover the free-streaming limit. The electrons and ions are allowed to have different temperatures and we include a flux limited electron heat conduction. The radiation hydrodynamic equations are solved in the Eulerian frame by means of a conservative finite volume discretization in either one, two, or three-dimensional slab geometry or in two-dimensional cylindrical symmetry. An operator split method is used to solve these equations in three substeps: (1) solve the hydrodynamic equations with shock-capturing schemes, (2) a linear advection of the radiation in frequency-logarithm space, and (3) an implicit solve of the stiff radiation diffusion, heat conduction, and energy exchange. We present a suite of verification test problems to demonstrate the accuracy and performance of the algorithms. The CRASH code is an extension of the Block-Adaptive Tree Solarwind Roe Upwind Scheme (BATS-R-US) code with this new radiation transfer and heat conduction library and equation-of-state and multigroup opacity solvers. Both CRASH and BATS-R-US are part of the publicly available Space Weather Modeling Framework (SWMF).

[ascl:2009.018]
CRAC: Cosmology R Analysis Code

CRAC (Cosmology R Analysis Code) provides R functions for cosmology. Its main functions are similar to the Python library CosmoloPy (ascl:2009.017); for example, it implements functions to compute spherical geometric quantities for cosmological research.

[ascl:2002.021]
CR-SISTEM: Symplectic integrator for lunar core-mantle and orbital dynamics

CR-SISTEM models lunar orbital and rotational dynamics, taking into account the effects of a liquid core. Orbits of the Moon and Earth are fully integrated, and other planets (or additional point-mass satellites) may be included in the integration. Lunar and solar tides on Earth, eccentricity and obliquity tides on the Moon, and lunar core-mantle friction are included. The integrator is one file (crsistem5.for) written in FORTRAN 90, uses seven input files (settings.in, planets.in, moons.in, tidal.in, lunar.in, precess.in and core.in), and has at least eight output files (planet101.out, moon101.out, pole.out, spin_orb.out, spin_ecl.out, cspin_ecl.out, long.out and clong.out); additional moons and planets would add more output. The input files provided with the code set up a 1 Myr simulation of a slow-spinning Moon on an orbit of 40 Earth radii, which will then dynamically relax to the lowest-energy state (in this case it is a synchronous rotation with a core spinning separately from the mantle).

[ascl:1102.012]
CPROPS: Bias-free Measurement of Giant Molecular Cloud Properties

CPROPS, written in IDL, processes FITS data cubes containing molecular line emission and returns the properties of molecular clouds contained within it. Without corrections for the effects of beam convolution and sensitivity to GMC properties, the resulting properties may be severely biased. This is particularly true for extragalactic observations, where resolution and sensitivity effects often bias measured values by 40% or more. We correct for finite spatial and spectral resolutions with a simple deconvolution and we correct for sensitivity biases by extrapolating properties of a GMC to those we would expect to measure with perfect sensitivity. The resulting method recovers the properties of a GMC to within 10% over a large range of resolutions and sensitivities, provided the clouds are marginally resolved with a peak signal-to-noise ratio greater than 10. We note that interferometers systematically underestimate cloud properties, particularly the flux from a cloud. The degree of bias depends on the sensitivity of the observations and the (u,v) coverage of the observations. In the Appendix to the paper we present a conservative, new decomposition algorithm for identifying GMCs in molecular-line observations. This algorithm treats the data in physical rather than observational units, does not produce spurious clouds in the presence of noise, and is sensitive to a range of morphologies. As a result, the output of this decomposition should be directly comparable among disparate data sets.

The CPROPS package contains within it a distribution of the CLUMPFIND code (ascl:1107.014) written by Jonathan Williams and described in Williams, de Geus, and Blitz (1994). If you make use of the CLUMPFIND functionality in the CPROPS package for a publication, please cite Jonathan's original article.

[ascl:1710.009]
CppTransport: Two- and three-point function transport framework for inflationary cosmology

CppTransport solves the 2- and 3-point functions of the perturbations produced during an inflationary epoch in the very early universe. It is implemented for models with canonical kinetic terms, although the underlying method is quite general and could be scaled to handle models with a non-trivial field-space metric or an even more general non-canonical Lagrangian.

[ascl:2205.021]
CPNest: Parallel nested sampling

CPNest performs Bayesian inference using the nested sampling algorithm. It is designed to be simple for the user to provide a model via a set of parameters, their bounds and a log-likelihood function. An optional log-prior function can be given for non-uniform prior distributions. The nested sampling algorithm is then used to compute the marginal likelihood or evidence. As a by-product the algorithm produces samples from the posterior probability distribution. The implementation is based on an ensemble MCMC sampler which can use multiple cores to parallelize computation.

[ascl:1402.010]
CPL: Common Pipeline Library

The Common Pipeline Library (CPL) is a set of ISO-C libraries that provide a comprehensive, efficient and robust software toolkit to create automated astronomical data reduction pipelines. Though initially developed as a standardized way to build VLT instrument pipelines, the CPL may be more generally applied to any similar application. The code also provides a variety of general purpose image- and signal-processing functions, making it an excellent framework for the creation of more generic data handling packages. The CPL handles low-level data types (images, tables, matrices, strings, property lists, etc.) and medium-level data access methods (a simple data abstraction layer for FITS files). It also provides table organization and manipulation, keyword/value handling and management, and support for dynamic loading of recipe modules using programs such as EsoRex (ascl:1504.003).

[ascl:1808.003]
CPF: Corral Pipeline Framework

Cabral, Juan; Sanchez, Bruno; Beroiz, Martin; Dominguez, Mariano; Lares, Marcelo; Gurovich, Sebastian; Granitto, Pablo

Corral generates astronomical pipelines. Data processing pipelines represent an important slice of the astronomical software library that include chains of processes that transform raw data into valuable information via data reduction and analysis. Written in Python, Corral features a Model-View-Controller design pattern on top of an SQL Relational Database capable of handling custom data models, processing stages, and communication alerts. It also provides automatic quality and structural metrics based on unit testing. The Model-View-Controller provides concept separation between the user logic and the data models, delivering at the same time multi-processing and distributed computing capabilities.

[ascl:2201.011]
COWS: Cosmic web filament finder

Pfeifer, Simon; Libeskind, Noam I.; Hoffman, Yehuda; Hellwing, Wojciech A.; Bilicki, Maciej; Naidoo, Krishna

COWS (COsmic Web Skeleton) implements the cosmic filament finder COsmic Web Skeleton (COWS). Written in Python, the cosmic filament finder works on Hessian-based cosmic web identifiers (such as the V-web) and returns a catalogue of filament spines. The code identifies the medial axis, or skeleton, of cosmic web filaments and then separates this skeleton into individual filaments.

[ascl:1904.028]
covdisc: Disconnected covariance of 2-point functions in large-scale structure of the Universe

covdisc computes the disconnected part of the covariance matrix of 2-point functions in large-scale structure studies, accounting for the survey window effect. This method works for both power spectrum and correlation function, and applies to the covariances for various probes including the multi- poles and the wedges of 3D clustering, the angular and the projected statistics of clustering and lensing, as well as their cross covariances.

[ascl:1512.013]
CounterPoint: Zeeman-split absorption lines

CounterPoint works in concert with MoogStokes (ascl:1308.018). It applies the Zeeman effect to the atomic lines in the region of study, splitting them into the correct number of Zeeman components and adjusting their relative intensities according to the predictions of Quantum Mechanics, and finally creates a Moog-readable line list for use with MoogStokes. CounterPoint has the ability to use VALD and HITRAN line databases for both atomic and molecular lines.

[ascl:1307.010]
cosmoxi2d: Two-point galaxy correlation function calculation

Cosmoxi2d is written in C and computes the theoretical two-point galaxy correlation function as a function of cosmological and galaxy nuisance parameters. It numerically evaluates the model described in detail in Reid and White 2011 (arxiv:1105.4165) and Reid et al. 2012 (arxiv:1203.6641) for the multipole moments (up to ell = 4) for the observed redshift space correlation function of biased tracers as a function of cosmological (though an input linear matter power spectrum, growth rate f, and Alcock-Paczynski geometric factors alphaperp and alphapar) as well as nuisance parameters describing the tracers (bias and small scale additive velocity dispersion, isotropicdisp1d).

This model works best for highly biased tracers where the 2nd order bias term is small. On scales larger than 100 Mpc, the code relies on 2nd order Lagrangian Perturbation theory as detailed in Matsubara 2008 (PRD 78, 083519), and uses the analytic version of Reid and White 2011 on smaller scales.

[ascl:1504.010]
CosmoTransitions: Cosmological Phase Transitions

CosmoTransitions analyzes early-Universe finite-temperature phase transitions with multiple scalar fields. The code enables analysis of the phase structure of an input theory, determines the amount of supercooling at each phase transition, and finds the bubble-wall profiles of the nucleated bubbles that drive the transitions.

[ascl:1311.009]
CosmoTherm: Thermalization code

CosmoTherm allows precise computation of CMB spectral distortions caused by energy release in the early Universe. Different energy-release scenarios (e.g., decaying or annihilating particles) are implemented using the Green's function of the cosmological thermalization problem, allowing fast computation of the distortion signal. The full thermalization problem can be solved on a case-by-case basis for a wide range of energy-release scenarios using the full PDE solver of CosmoTherm. A simple Monte-Carlo toolkit is included for parameter estimation and forecasts using the Green's function method.

[ascl:1701.004]
CosmoSlik: Cosmology sampler of likelihoods

CosmoSlik quickly puts together, runs, and analyzes an MCMC chain for analysis of cosmological data. It is highly modular and comes with plugins for CAMB (ascl:1102.026), CLASS (ascl:1106.020), the Planck likelihood, the South Pole Telescope likelihood, other cosmological likelihoods, emcee (ascl:1303.002), and more. It offers ease-of-use, flexibility, and modularity.

[ascl:1409.012]
CosmoSIS: Cosmological parameter estimation

Zuntz, Joe; Paterno, Marc; Jennings, Elise; Rudd, Douglas; Manzotti, Alessandro; Dodelson, Scott; Bridle, Sarah; Sehrish, Saba; Kowalkowski, James

CosmoSIS is a cosmological parameter estimation code. It structures cosmological parameter estimation to ease re-usability, debugging, verifiability, and code sharing in the form of calculation modules. Witten in python, CosmoSIS consolidates and connects existing code for predicting cosmic observables and maps out experimental likelihoods with a range of different techniques.

[ascl:2401.005]
CosmosCanvas: Useful color maps for different astrophysical properties

CosmosCanvas creates perception-based color maps for different astrophysical properties such as spectral index and velocity fields. Three tutorials demonstrate how to use python code to exploit and adjust the boundaries in these divergent colour schemes. Intended to work with human physiology, each tutorial offers at least one default scheme that is monotonic in value both as a redundancy for supporting data information and an aid for colour blind viewers. This library relies on Gilles Ferrand's colourspace library.

[ascl:1705.001]
COSMOS: Carnegie Observatories System for MultiObject Spectroscopy

COSMOS (Carnegie Observatories System for MultiObject Spectroscopy) reduces multislit spectra obtained with the IMACS and LDSS3 spectrographs on the Magellan Telescopes. It can be used for the quick-look analysis of data at the telescope as well as for pipeline reduction of large data sets. COSMOS is based on a precise optical model of the spectrographs, which allows (after alignment and calibration) an accurate prediction of the location of spectra features. This eliminates the line search procedure which is fundamental to many spectral reduction programs, and allows a robust data pipeline to be run in an almost fully automatic mode, allowing large amounts of data to be reduced with minimal intervention.

[ascl:1304.017]
CosmoRec: Cosmological Recombination code

CosmoRec solves the recombination problem including recombinations to highly excited states, corrections to the 2s-1s two-photon channel, HI Lyn-feedback, n>2 two-photon profile corrections, and n≥2 Raman-processes. The code can solve the radiative transfer equation of the Lyman-series photon field to obtain the required modifications to the rate equations of the resolved levels, and handles electron scattering, the effect of HeI intercombination transitions, and absorption of helium photons by hydrogen. It also allows accounting for dark matter annihilation and optionally includes detailed helium radiative transfer effects.

[ascl:2405.025]
CosmoPower: Machine learning-accelerated Bayesian inference

CosmoPower develops Bayesian inference pipelines that leverage machine learning to solve inverse problems in science. While the emphasis is on building algorithms to accelerate Bayesian inference in cosmology, the implemented methods allow for their application across a wide range of scientific fields. CosmoPower provides neural network emulators of matter and Cosmic Microwave Background power spectra, which can replace Boltzmann codes such as CAMB (ascl:1102.026) or CLASS (ascl:1106.020) in cosmological inference pipelines, to source the power spectra needed for two-point statistics analyses. This provides orders-of-magnitude acceleration to the inference pipeline and integrates naturally with efficient techniques for sampling very high-dimensional parameter spaces.

[ascl:1212.006]
CosmoPMC: Cosmology sampling with Population Monte Carlo

Kilbinger, Martin; Benabed, Karim; Cappé, Olivier; Coupon, Jean; Cardoso, Jean-François; Fort, Gersende; McCracken, Henry Joy; Prunet, Simon; Robert, Christian P.; Wraith, Darren

CosmoPMC is a Monte-Carlo sampling method to explore the likelihood of various cosmological probes. The sampling engine is implemented with the package pmclib. It is called Population MonteCarlo (PMC), which is a novel technique to sample from the posterior. PMC is an adaptive importance sampling method which iteratively improves the proposal to approximate the posterior. This code has been introduced, tested and applied to various cosmology data sets.

[ascl:1408.018]
CosmoPhotoz: Photometric redshift estimation using generalized linear models

de Souza, Rafael S.; Elliott, Jonathan; Krone-Martins, Alberto; Ishida, Emille E. O.; Hilbe, Joseph; Cameron, Ewan

CosmoPhotoz determines photometric redshifts from galaxies utilizing their magnitudes. The method uses generalized linear models which reproduce the physical aspects of the output distribution. The code can adopt gamma or inverse gaussian families, either from a frequentist or a Bayesian perspective. A set of publicly available libraries and a web application are available. This software allows users to apply a set of GLMs to their own photometric catalogs and generates publication quality plots with no involvement from the user. The code additionally provides a Shiny application providing a simple user interface.

[ascl:2001.010]
CosMOPED: Compressed Planck likelihood

CosMOPED (Cosmological MOPED) uses the MOPED (Multiple/Massively Optimised Parameter Estimation and Data compression) compression scheme to compress the Planck power spectrum. This convenient and lightweight compressed likelihood code is implemented in Python. To compute the likelihood for the LambdaCDM model using CosMOPED, one needs only six compression vectors, one for each parameter, and six numbers from compressing the Planck data using the six compression vectors. Using these, the likelihood of a theory power spectrum given the Planck data is the product of six one-dimensional Gaussians. Extended cosmological models require computing extra compression vectors.

[ascl:1110.019]
CosmoNest: Cosmological Nested Sampling

CosmoNest is an algorithm for cosmological model selection. Given a model, defined by a set of parameters to be varied and their prior ranges, and data, the algorithm computes the evidence (the marginalized likelihood of the model in light of the data). The Bayes factor, which is proportional to the relative evidence of two models, can then be used for model comparison, i.e. to decide whether a model is an adequate description of data, or whether the data require a more complex model.

For convenience, CosmoNest, programmed in Fortran, is presented here as an optional add-on to CosmoMC (ascl:1106.025), which is widely used by the cosmological community to perform parameter fitting within a model using a Markov-Chain Monte-Carlo (MCMC) engine. For this reason it can be run very easily by anyone who is able to compile and run CosmoMC. CosmoNest implements a different sampling strategy, geared for computing the evidence very accurately and efficiently. It also provides posteriors for parameter fitting as a by-product.

[ascl:1106.025]
CosmoMC: Cosmological MonteCarlo

We present a fast Markov Chain Monte-Carlo exploration of cosmological parameter space. We perform a joint analysis of results from recent CMB experiments and provide parameter constraints, including sigma_8, from the CMB independent of other data. We next combine data from the CMB, HST Key Project, 2dF galaxy redshift survey, supernovae Ia and big-bang nucleosynthesis. The Monte Carlo method allows the rapid investigation of a large number of parameters, and we present results from 6 and 9 parameter analyses of flat models, and an 11 parameter analysis of non-flat models. Our results include constraints on the neutrino mass (m_nu < 0.3eV), equation of state of the dark energy, and the tensor amplitude, as well as demonstrating the effect of additional parameters on the base parameter constraints. In a series of appendices we describe the many uses of importance sampling, including computing results from new data and accuracy correction of results generated from an approximate method. We also discuss the different ways of converting parameter samples to parameter constraints, the effect of the prior, assess the goodness of fit and consistency, and describe the use of analytic marginalization over normalization parameters.

[ascl:1110.024]
CosmoMC SNLS: CosmoMC Plug-in to Analyze SNLS3 SN Data

This module is a plug-in for CosmoMC and requires that software. Though programmed to analyze SNLS3 SN data, it can also be used for other SN data provided the inputs are put in the right form. In fact, this is probably a good idea, since the default treatment that comes with CosmoMC is flawed. Note that this requires fitting two additional SN nuisance parameters (alpha and beta), but this is significantly faster than attempting to marginalize over them internally.

[ascl:2009.017]
CosmoloPy: Cosmology package for Python

CosmoloPy is a suite of cosmology routines built on NumPy/SciPy. Its capabilities include various cosmological densities, distance measures, and galaxy luminosity functions (Schecter functions). It also offers pre-defined sets of cosmological parameters (*e.g.*, from WMAP), conversion in and out of the AB magnitude system, and the reionization of the IGM. Functions take cosmological parameters (which can be numpy arrays) as keywords and ignore any extra keywords, making it possible to build a dictionary of cosmological parameters and pass it to any function.

[ascl:2006.006]
CosmoLike: Cosmological Likelihood analyses

CosmoLike analyzes cosmological data sets and forecasts future missions. It has been used in the analysis of the Dark Energy Survey and to optimize the Large Synoptic Survey Telescope and the Wide-Field Infrared Survey Telescope, and is useful for innovative theory projects that test new concepts and methods to enhance the constraining power of cosmological analyses.

[ascl:2312.007]
CosmoLED: Cosmo code for Large Extra Dimension (LED) black holes

CosmoLED computes Hawking evaporation from black holes and set constraints on the fraction of black holes in dark matter. Based on ExoCLASS (ascl:1106.020), the code provides a DarkAges_LED module and C codes in class_LED to compute the evolution and energy deposition functions from LED black holes. Though CosmoLED is designed for large extra dimension black holes, it can also be used to study 4D black holes.

[ascl:2311.012]
CosmoLattice: Lattice simulator of scalar and gauge field dynamics in an expanding universe

CosmoLattice performs lattice simulations of field dynamics in an expanding universe. The code can simulate the dynamics of interacting scalar field theories, Abelian U(1) gauge theories, and non-Abelian SU(2) gauge theories, either in flat spacetime or an expanding FLRW background, including the case of self-consistent expansion sourced by the fields themselves. It can also compute gravitational waves sourced by U(1) Abelian Gauge fields. The CosmoLattice platform can implement any system of dynamical equations suitable for discretization on a lattice, as it introduces its own language describing fields and operations between them, and hence can implement new libraries to solve arbitrary field problems (related or not to cosmology).

[ascl:1303.003]
CosmoHammer: Cosmological parameter estimation with the MCMC Hammer

CosmoHammer is a Python framework for the estimation of cosmological parameters. The software embeds the Python package emcee by Foreman-Mackey et al. (2012) and gives the user the possibility to plug in modules for the computation of any desired likelihood. The major goal of the software is to reduce the complexity when one wants to extend or replace the existing computation by modules which fit the user's needs as well as to provide the possibility to easily use large scale computing environments. CosmoHammer can efficiently distribute the MCMC sampling over thousands of cores on modern cloud computing infrastructure.

[ascl:2306.032]
CosmoGraphNet: Cosmological parameters and galaxy power spectrum from galaxy catalogs

CosmoGraphNet infers cosmological parameters or the galaxy power spectrum. It creates a graph from a galaxy catalog with information the 3D position and intrinsic galactic properties. A Graph Neural Network is then applied to predict the cosmological parameters or the galaxy power spectrum.

[ascl:2007.023]
CosmoGRaPH: Cosmological General Relativity and (Perfect fluid | Particle) Hydrodynamics

CosmoGRaPH explores cosmological problems in a fully general relativistic setting. Written in C++, it implements various novel methods for numerically solving the Einstein field equations, including an N-body solver, full AMR capabilities via SAMRAI, and raytracing.

[ascl:2009.020]
cosmoFns: Functions for observational cosmology

cosmoFns computes distances, times, luminosities, and other quantities useful in observational cosmology, including molecular line observations. Written in R and coded for a flat universe, it contains functions for rest-frame line and luminosities, cosmic lookback time given z and cosmological parameters, and differential comoving volume. cosmoFns also computes comoving, luminosity, and angular diameter distances and molecular mass, among other quantities.

[ascl:2006.005]
CosmoCov: Configuration space covariances for projected galaxy 2-point statistics

CosmoCov computes configuration space covariances for projected galaxy 2-point statistics based on the CosmoLike (ascl:2006.006) framework. The package provides a flat sky covariance module, computed with the 2D-FFTLog (ascl:2006.004) algorithm, and a curved sky covariance module.

[ascl:1511.019]
CosmoBolognaLib: Open source C++ libraries for cosmological calculations

CosmoBolognaLib contains numerical libraries for cosmological calculations; written in C++, it is intended to define a common numerical environment for cosmological investigations of the large-scale structure of the Universe. The software aids in handling real and simulated astronomical catalogs by measuring one-point, two-point and three-point statistics in configuration space and performing cosmological analyses. These open source libraries can be included in either C++ or Python codes.

[ascl:1505.013]
cosmoabc: Likelihood-free inference for cosmology

Ishida, Emille E. O.; Vitenti, Sandro D. P.; Penna-Lima, Mariana; Trindade, Arlindo M.; Cisewski, Jessi; de Souza, Rafael M.; Cameron, Ewan; Busti, Vinicius C.

Approximate Bayesian Computation (ABC) enables parameter inference for complex physical systems in cases where the true likelihood function is unknown, unavailable, or computationally too expensive. It relies on the forward simulation of mock data and comparison between observed and synthetic catalogs. cosmoabc is a Python Approximate Bayesian Computation (ABC) sampler featuring a Population Monte Carlo variation of the original ABC algorithm, which uses an adaptive importance sampling scheme. The code can be coupled to an external simulator to allow incorporation of arbitrary distance and prior functions. When coupled with the numcosmo library, it has been used to estimate posterior probability distributions over cosmological parameters based on measurements of galaxy clusters number counts without computing the likelihood function.

[ascl:9910.004]
COSMICS: Cosmological initial conditions and microwave anisotropy codes

COSMICS is a package of Fortran programs useful for computing transfer functions and microwave background anisotropy for cosmological models, and for generating gaussian random initial conditions for nonlinear structure formation simulations of such models. Four programs are provided: linger_con and linger_syn integrate the linearized equations of general relativity, matter, and radiation in conformal Newtonian and synchronous gauge, respectively; deltat integrates the photon transfer functions computed by the linger codes to produce photon anisotropy power spectra; and grafic tabulates normalized matter power spectra and produces constrained or unconstrained samples of the matter density field.

[ascl:1601.008]
CosmicPy: Interactive cosmology computations

CosmicPy performs simple and interactive cosmology computations for forecasting cosmological parameters constraints; it computes tomographic and 3D Spherical Fourier-Bessel power spectra as well as Fisher matrices for galaxy clustering. Written in Python, it relies on a fast C++ implementation of Fourier-Bessel related computations, and requires NumPy, SciPy, and Matplotlib.

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