Observational and Theoretical Cosmology

Cosmology Parameters and Model Optimization

• CCL - DESC Core Cosmology Library: cosmology routines with validated numerical accuracy
  • On top of CCL, there is firecrown:
  • firecrown: the “c” is for “cosmology”
• CosmoMC - MCMC parameter sampling code
  • CosmoMC is a Fortran 2008 Markov-Chain Monte-Carlo (MCMC) engine for exploring cosmological parameter space, together with Fortran and python code for analysing Monte-Carlo samples and importance sampling (plus a suite of scripts for building grids of runs, plotting and presenting results).
• CosmoHammer - Cosmological parameter estimation with the MCMC Hammer
  • A paper describing the software can be found here
• Cobaya - Code for Bayesian Analysis in Cosmology
  • Cobaya is a framework for sampling and statistical modelling: it allows you to explore an arbitrary prior or posterior using a range of Monte Carlo samplers (including the advanced MCMC sampler from CosmoMC, and the advanced nested sampler PolyChord).
• MontePython - The Monte Carlo code for class in Python
  • MontePython is a Monte Carlo code for Cosmological Parameter extraction. It contains likelihood codes of most recent experiments, and interfaces with the Boltzmann code Class for computing the cosmological observables.

Supernova related

• sncosmo - Python library for supernova cosmology
  • SNCosmo is a Python library for supernova cosmology analysis. It aims to make such analysis both as flexible and clear as possible. Online document is here
  • sndatasets - Download and normalize published supernova photometric data

CMB related

• CAMB - Code for Anisotropies in the Microwave Background
  • CAMB is a cosmology code for calculating cosmological observables, including CMB, lensing, source count and 21cm angular power spectra, matter power spectra, transfer functions and background evolution
• CLassylss - a lightweight Python binding of the CLASS CMB Boltzmann code
  • A very nice gateway to CLASS
• CLASS - Cosmic Linear Anisotropy Solving System
  • The purpose of CLASS is to simulate the evolution of linear perturbations in the universe and to compute CMB and large scale structure observables.
  • A public repository is available on github

Correlation Functions

• Corrfunc - Blazing fast correlation functions on the CPU
• TreeCorr - Code for efficiently computing 2-point and 3-point correlation functions
  • It can compute correlations of regular number counts, weak lensing shears, or scalar quantities such as convergence or CMB temperature fluctutations.

Weak Lensing

• Both corrfunc and treecorr can be used for shear-shear or galaxy-shear analysis
• LensTools - collects together a suite of widely used analysis tools in Weak Gravitational Lensing
• DESWL - A collection of scripts and software related to DES weak lensing analysis
  • By Marc Jarvis

Cluster Lensing

• cluster-lensing - Galaxy Cluster and Weak Lensing Tools
  • By Jes Ford. Paper can be found here
• cluster_toolkit - Tools for analyzing galaxy clusters
  • by Tom McClintock. Contains routines used in the Dark Energy Survey Year 1 stacked cluster weak lensing analysis.

IGM Related (e.g. Lya Forrest)

• IGMHUB - IGM analysis tools
  • baofit - Fits cosmological data to measure baryon acoustic oscillations
    • baofit is a software package for analyzing cosmological correlation functions to estimate parameters related to baryon acoustic oscillations and redshift-space distortions

Dark Matter Halos

• Halotools - Python package for studying large scale structure, cosmology, and galaxy evolution using N-body simulations and halo models
  • Halotools is a specialized python package for building and testing models of the galaxy-halo connection, and analyzing catalogs of dark matter halos.
• Colossus - a python toolkit for calculations pertaining to cosmology, the large-scale structure of the universe, and the properties of dark matter halos
• COMMAH - COncentration-Mass relation and Mass Accretion History
  • By Camila Correa; calculates dark matter halo concentrations as a function of halo mass and redshift. The source code is available on Github
  • Based on the works of Correa et al. 2015a; Correa et al. 2015c

Emulators:

• Increasingly popular way to study cosmology based on a limit set of N-body simulations.

Key Technique

• A suite of N-body cosmology simulations
  • 2nd order Lagrangian perturbation theory (2LPT) initial conditions
    • e.g. by 2LPTIC or on Github here
  • Input power spectrum. e.g. by CAMB: Code for Anisotropies in the Microwave Background
• Sampling the cosmological parameters:
  • Latin Hypercube Designs (LHDs)
  • Maximin-distance “sliced” LHD (SLHD)
    • A Python implementation
    • SMT - Surrogate Modeling Toolbox
    • Another Python version
• Principle Component Analysis (PCA)
  • e.g. empca by Stephen Bailey
• Gaussian process emulator
  • e.g. george by Dan Foreman-Mackey

Available Emulators

• Aemulus Project led by Stanford
  • The basic structure of the code: Aemulator
  • Emulator of halo mass function and halo bias
  • The Aemulus Project I: Numerical Simulations for Precision Cosmology
  • The Aemulus Project II: Emulating the Halo Mass Function
  • The Aemulus Project III: Emulation of the Galaxy Correlation Function
  • Documents for data release 1
• CosmicEmu led by ANL
  • Code can be found here
  • CosmicEmu produces predictions for the matter power spectrum based on eight cosmological parametersand redshift.
  • Based on the Mira-Titan simulations
  • Also related to the Coyote Universe emulator: Paper I, Paper II, Paper III, and Extended
  • Paper about the emulated power-spectrum
  • Paper about the emulated halo mass-concentration relation
• ACME Emulator led by OSU
  • Paper by Ben Wibking: Emulating galaxy clustering and galaxy-galaxy lensing into the deeply nonlinear regime
  • Use the AbacusCosmos suite of simulations
    • The code used for the simulation is here
    • The AbacusCosmos description paper
• Dark Emulator led by IPMU
  • Based on the Dark Quest suite of simulations.
  • Dark Quest. I. Fast and Accurate Emulation of Halo Clustering Statistics and Its Application to Galaxy Clustering