Standard Library Overview

The CosmoSIS standard library is a collection of modules designed for Cosmological parameter estimation. You can couple together pieces of it to build analysis piplines.

Background

These modules calculate quantities related to the average background expansion of the Universe.

Name	Purpose
distances	Output cosmological distance measures for dynamical dark energy
growth_factor	returns linear growth factor and growth rate for flat cosmology with either const w or variable DE eos w(a) = w + (1-a)*wa
astropy_background	Calculate background cosmology using astropy
log_w_model	Implement Tripathi, Sangwan, Jassal (2017) w(z) model

Boltzmann

Boltzmann codes evolve cosmic perturbations from the early Universe through recombination and to late times, and power spectra of matter, the CMB, and other quantities.

Name	Purpose
mgcamb	Modified Gravity Boltzmann and background integrator for BG, CMB, and matter power
isitgr-camb	Modified version of CAMB to implement phenomenological modified gravity models
class	Boltzmann and background integrator for BG, CMB, matter power, and more
camb	Boltzmann and background integrator for BG, CMB, and matter power

Emulators

These modules emulate aspects of cosmic structure based on fits to simulations.

Name	Purpose
bacco_emulator	Emulate the non-linear, baryonified, matter power spectrum
FrankenEmu	Emulate N-body simulations to compute nonlinear matter power
EuclidEmulator2	Emulate the boost factors that convert the linear to non-linear power spectrum, including baryon corrections
CosmicEmu	Emulate N-body simulations to compute nonlinear matter power

Structure

These modules compute aspects of cosmic structure, for example by integrating over cosmic structure, or calculating halo model quantities.

Name	Purpose
sigma_r	Compute anisotropy dispersion sigma(R,z)
extract_growth	returns growth factor and growth rate by examining small-scale P(k)
NLfactor	Compute nonlinear weyl potential (and other) spectrum by multiplying the linear spectrum with matter_power_nl/matter_power_lin
constant_bias	Apply a galaxy bias constant with k and z.
sigma_cpp	Compute anisotropy dispersion sigma(R,z) in cpp
Press_Schechter_MF	Code to compute the PressSchechter mass function given Pk from CAMB, based on Komatsu’s CRL
Extreme_Value_Statistics	PDF of the maximum cluster mass given cosmological parameters
pyhmcode	Compute the non-linear matter power spectrum with pyhalofit
extrapolate	Simple log-linear extrapolation of P(k) to high k
CRL_Eisenstein_Hu	Komatsu’s CRL code to compute the power spectrum using EH fitting formula.
Tinker_MF	Code to compute the Tinker et al. mass function given Pk from CAMB, based on Komatsu’s CRL
Sheth-Tormen MF	Code to compute the Sheth-Tormen mass function given Pk from CAMB, based on Komatsu’s CRL

Two-point Mathemetics

These modules perform mathematical claculations associated with two-point statistics, mostly on a sphere.

Name	Purpose
cosebis	Calculate COSEBIs from C_ell power spectra
wl_spectra_ppf	Compute weak lensing C_ell from P(k,z) and MG D(k,z) with the Limber integral
cl_to_xi_wigner_d	Compute correlation functions from power spectra
cl_to_corr	Compute correlation functions xi+, xi-, w, and gamma_t from C_ell
project_2d	Project 3D power spectra to 2D tomographic bins using the Limber approximation
cl_to_xi_nicaea	Compute WL correlation functions xi+, xi- from C_ell
wl_spectra	Compute various weak lensing C_ell from P(k,z) with the Limber integral

Two-point Systematics

These modules compute and apply quantities associated with systematics errors on two-point (and potentially other) quantities.

Name	Purpose
shear_bias	Modify a set of calculated shear C_ell with a multiplicative bias
add_magnification	Add magnification terms to C_ell
clerkin	Compute galaxy bias as function of k, z for 3-parameter Clerkin et al 2014 model
constant_bias	Apply a galaxy bias constant with k and z.
ia_z_powerlaw	Add redshift dependence to IA model
kappa_beam	Apply smoothing function to cross-correlations with CMB kappa in harmonic space.
no_bias	Generate galaxy power P(k) as though galaxies were unbiased DM tracers
baryonic	Apply baryonic effects to nonlinear pk based on hydrodynamic simulation measurements
kappa_ell_cut	Apply minimum and maximum ell to cross-power spectra with CMB kappa.
add_intrinsic	Sum together intrinsic aligments with shear signal
apply_astrophysical_biases	Apply various astrophysical biases to the matter power spectrum P(k,z)
linear_alignments	Compute the terms P_II and P_GI which go into intrinsic aligment calculations

Sample Properties

These modules compute properties, mostly number density, of galaxy samples.

Name	Purpose
smail	Compute window functions for photometric n(z)
photoz_bias	Modify a set of loaded n(z) distributions with a multiplicative or additive bias
Joachimi_Bridle_alpha	Calculate the gradient of the galaxy luminosity function at the limiting magnitude of the survey.
gaussian_window	Compute Gaussian n(z) window functions for weak lensing bins
nz_multirank	Load, rank, and sample a set of density n(z) realisations from a FITS file
load_nz	Load a number density n(z) for weak lensing from a file
load_nz_fits	Load a number density n(z) from a FITS file

Likelihoods

These module provide likelihoods that compare theory predictions to data

Name	Purpose
Cluster_mass	Likelihood of z=1.59 Cluster mass from Santos et al. 2011
des-y3-bao	Compute the likelihood of DES Y3 BAO data
eboss_dr16_qso	Compute the likelihood of eBOSS DR16 from QSO
BOSS	Compute the likelihood of supplied fsigma8(z=0.57), H(z=0.57), D_a(z=0.57), omegamh2, bsigma8(z=0.57)
h0licow
2pt	Generic 2-point measurement Gaussian likelihood
mgs_bao	Compute the likelihood against SDSS MGS data
Riess16	Likelihood of hubble parameter H0 from Riess et al 2.4% supernova sample
eboss_dr16_lya	Compute the likelihood of eBOSS DR16 from Lyman alpha
planck_sz	Prior on sigma_8 * Omega_M ** 0.3 from Planck SZ cluster counts
eboss_dr14_lya	Compute the likelihood of eBOSS DR14 D_m and D_h from Lyman alpha
planck2018	Likelihood function of CMB from Planck 2015 data
Riess21	Likelihood of hubble parameter H0 from Riess et al supernova sample
WiggleZBao	Compute the likelihood of the supplied expansion history against WiggleZ BAO data
wmap	Likelihood function of CMB from WMAP
pantheon	Likelihood of the Pantheon supernova analysis
boss_dr12	Compute the likelihood of the supplied expansion and growth history against BOSS DR12 data
mgs	Compute the likelihood of MGS BAO and FS as distributed by eBOSS DR16
jla	Supernova likelihood for SDSS-II/SNLS3
qso	Compute the likelihood of eBOSS DR14 D_v from QSO
boss_dr12_lrg_reanalyze	Compute the likelihood of the supplied expansion and growth history against BOSS DR12 data as reanalyzed by eBOSS DR16
BBN	Simple prior on Omega_b h^2 from light element abundances
6dFGS	Compute the likelihood of supplied D_v or fsigma8(z=0.067)
strong_lens_time_delays
act-dr6-lens	CMB Lensing from ACT DR6 data.
planck_py	Lightweight python-based Planck likelihood code
fgas	Likelihood of galaxy cluster gas-mass fractions
eboss_dr16_lrg	Compute the likelihood of eBOSS DR16 from LRG
lrg	Compute the likelihood of eBOSS DR14 D_v from LRG
pantheon_plus	Likelihood of the Pantheon+ supernova analysis optionally combined with the SH0ES H0 measurement
eboss_dr16_elg	Compute the likelihood of eBOSS DR16 from ELG
JulloLikelihood	Likelihood of Jullo et al (2012) measurements of a galaxy bias sample
Riess11	Likelihood of hubble parameter H0 from Riess et al supernova sample
balmes
wmap_shift	Massively simplified WMAP9 likelihood reduced to just shift parameter
BICEP2	Compute the likelihood of the supplied CMB power spectra

Misc & Utilities

These modules supply special utilities or calculation tools

Name	Purpose
fast_pt	Compute various 1-loop perturbation theory quantities
sigma8_rescale	Rescale structure measures to use a specified sigma_8
stop	Enters python debugger.
copy	Copy a section to a new section
BBN-Consistency	Compute consistent Helium fraction from baryon density given BBN
rename	Rename a section to a new name
correlated_priors	Include correlations between nusiance parameters
w0wa_sum_prior	Skip parameter sample without failing if w0+wa>0.
delete	Enters python debugger.
consistency	Deduce missing cosmological parameters and check consistency