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
astropy_background	Calculate background cosmology using astropy
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
log_w_model	Implement Tripathi, Sangwan, Jassal (2017) w(z) model
rescale_distances_rdh	Rescale computed distances to be consistent with a given value of R_d * h

Baryons

These modules modify matter power spectra to account for the effects of baryonic physics.

Name	Purpose
amod	Modify the non-linear matter power spectrum using the A_mod phenomenological parameterization
baryonic	Apply baryonic effects to nonlinear pk based on hydrodynamic simulation measurements
owls	Apply baryonic feedback effects to matter power spectrum using OWLS simulations

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
camb	Boltzmann and background integrator for BG, CMB, and matter power
class	Boltzmann and background integrator for BG, CMB, matter power, and more
isitgr-camb	Modified version of CAMB to implement phenomenological modified gravity models
mgcamb	Modified Gravity 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
CosmicEmu	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
FrankenEmu	Emulate N-body simulations to compute nonlinear matter power
bacco_emulator	Emulate the non-linear, baryonified, matter power spectrum

Structure

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

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

Two-point Mathemetics

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

Name	Purpose
cl_to_corr	Compute correlation functions xi+, xi-, w, and gamma_t from C_ell
cl_to_xi_fullsky	Transform angular power spectra C_ell to real-space correlation functions
cl_to_xi_nicaea	Compute WL correlation functions xi+, xi- from C_ell
cl_to_xi_wigner_d	Compute correlation functions from power spectra
cosebis	Calculate COSEBIs from C_ell power spectra
hmcode_eta	Compute eta parameter for HMCode based on concentration amplitude A
project_2d	Project 3D power spectra to 2D tomographic bins using the Limber approximation
wl_spectra	Compute various weak lensing C_ell from P(k,z) with the Limber integral
wl_spectra_ppf	Compute weak lensing C_ell from P(k,z) and MG D(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
add_gammat_point_mass	Add point mass contributions to galaxy-galaxy lensing (gamma_t) signal
add_intrinsic	Sum together intrinsic aligments with shear signal
add_magnification	Add magnification terms to C_ell
additive_systematic	Model additive systematic errors for HSC cosmic shear analysis
apply_astrophysical_biases	Apply various astrophysical biases to the matter power spectrum P(k,z)
binwise_bias	Apply galaxy bias on a per-bin basis to galaxy power spectra
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.
kappa_ell_cut	Apply minimum and maximum ell to cross-power spectra with CMB kappa.
linear_alignments	Compute the terms P_II and P_GI which go into intrinsic aligment calculations
no_bias	Generate galaxy power P(k) as though galaxies were unbiased DM tracers
shear_bias	Modify a set of calculated shear C_ell with a multiplicative bias
tatt	Compute intrinsic alignment power spectra using Tidal Alignment + Tidal Torquing model

Sample Properties

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

Name	Purpose
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
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
load_nz_sacc	Load number density n(z) from a SACC file for weak lensing
nz_multirank	Load, rank, and sample a set of density n(z) realisations from a FITS file
photoz_bias	Modify a set of loaded n(z) distributions with a multiplicative or additive bias
photoz_distortion	Apply photometric redshift systematic biases to n(z) distributions
smail	Compute window functions for photometric n(z)

CMB Likelihoods

These modules provide likelihoods that compare theory predictions to CMB data

Name	Purpose
BICEP2	Compute the likelihood of the supplied CMB power spectra
act-dr6-lens	CMB Lensing from ACT DR6 data.
act_dr6	Full Multi-Frequency primary CMB from ACT DR6 data including systematics and foregrounds.
act_dr6_lite	Foreground-marginalised primary CMB (CMB-only) from ACT DR6 data.
candl	Interface with candl.
hillipop	Likelihood of the Planck high-ell data from the NPIPE re-analysis, using the Hillipop likelihood code.
lollipop	Likelihood of the Planck low-ell data polarizaton data from the PR4 analysis, using the Lollipop likelihood code.
planck2018	Likelihood function of CMB from Planck 2015 data
planck_npipe	Planck NPIPE CMB lensing likelihood
planck_py	Lightweight python-based Planck likelihood code
planck_sz	Prior on sigma_8 * Omega_M ** 0.3 from Planck SZ cluster counts
wmap	Likelihood function of CMB from WMAP
wmap_shift	Massively simplified WMAP9 likelihood reduced to just shift parameter

BAO Likelihoods

These modules provide likelihoods that compare theory predictions to BAO data

Name	Purpose
6dFGS	Compute the likelihood of supplied D_v or fsigma8(z=0.067)
BOSS	Compute the likelihood of supplied fsigma8(z=0.57), H(z=0.57), D_a(z=0.57), omegamh2, bsigma8(z=0.57)
WiggleZBao	Compute the likelihood of the supplied expansion history against WiggleZ BAO data
boss_dr12	Compute the likelihood of the supplied expansion and growth history against BOSS DR12 data
boss_dr12_lrg_reanalyze	Compute the likelihood of the supplied expansion and growth history against BOSS DR12 data as reanalyzed by eBOSS DR16
des-y3-bao	Compute the likelihood of DES Y3 BAO data
des-y6-bao	Compute the likelihood of DES Y6 BAO data
des-y6-bao-5bins	Compute the likelihood of DES Y6 BAO data using individual redshift bins
desi_dr1	DESI BAO likelihood from DR1 data
desi_dr1_arxiv	DESI BAO likelihood from DR1 data
desi_dr2	DESI BAO likelihood from DR2 data
eboss_dr14_lya	Compute the likelihood of eBOSS DR14 D_m and D_h from Lyman alpha
eboss_dr16_elg	Compute the likelihood of eBOSS DR16 from ELG
eboss_dr16_lrg	Compute the likelihood of eBOSS DR16 from LRG
eboss_dr16_lya	Compute the likelihood of eBOSS DR16 from Lyman alpha
eboss_dr16_qso	Compute the likelihood of eBOSS DR16 from QSO
lrg	Compute the likelihood of eBOSS DR14 D_v from LRG
mgs	Compute the likelihood of MGS BAO and FS as distributed by eBOSS DR16
mgs_bao	Compute the likelihood against SDSS MGS data
qso	Compute the likelihood of eBOSS DR14 D_v from QSO

Supernova Likelihoods

These modules provide likelihoods that compare theory predictions to supernova data

Name	Purpose
des-y5-sn	Compute the likelihood of DES Y5 Supernova data
jla	Supernova likelihood for SDSS-II/SNLS3
pantheon	Likelihood of the Pantheon supernova analysis
pantheon_plus	Likelihood of the Pantheon+ supernova analysis optionally combined with the SH0ES H0 measurement
salt2	SALT2 supernova distance modulus likelihood

Cepheid Likelihoods

These modules provide likelihoods that compare theory predictions to Cepheid data

Name	Purpose
Riess11	Likelihood of hubble parameter H0 from Riess et al supernova sample
Riess16	Likelihood of hubble parameter H0 from Riess et al 2.4% supernova sample
Riess21	Likelihood of hubble parameter H0 from Riess et al supernova sample

Lensing and Clustering Likelihoods

These modules provide likelihoods that compare theory predictions to weak lensing and clustering data

Name	Purpose
2pt	Generic 2-point measurement Gaussian likelihood
hsc_cosmic_shear	Likelihoods of the HSC Year 3 cosmic shear data
sacc_like	Generic 2-point measurement Gaussian likelihood using sacc format
simple_like	Generate a simple 2pt likelihood given data, theory and covariance with no cuts

Strong Lensing Likelihoods

These modules provide likelihoods that compare theory predictions to strong lensing data

Name	Purpose
balmes	Balmes & Corasaniti 2012 H0 Measurement likelihood
h0licow	H0licow 2019 strong lensing likelihood
strong_lens_time_delays	Time delay likelihood for strong lensing systems from COSMOGRAIL 2017
tdcosmo	Likelihood of the TDCOSMO analyses

Other Likelihoods

These module provide likelihoods that compare theory predictions to other data

Name	Purpose
BBN	Simple prior on Omega_b h^2 from light element abundances
Cluster_mass	Likelihood of z=1.59 Cluster mass from Santos et al. 2011
JulloLikelihood	Likelihood of Jullo et al (2012) measurements of a galaxy bias sample
fgas	Likelihood of galaxy cluster gas-mass fractions

Misc & Utilities

These modules supply special utilities or calculation tools

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

Others

Modules that may be obsolete or only useful for a very specific project

Name	Purpose
add_colours	Combine red and blue galaxy intrinsic alignment signals based on color fractions
choose_ia	Select intrinsic alignment parameters from suffixed versions
cosmopower	Interface to cosmopower emulators for fast cosmological calculations, a drop-in replacement for camb.
delta_window	Compute Delta n(z) window functions for weak lensing bins
des-y6-bao-dr1tilesnoDESI	Compute the likelihood of DES Y6 BAO data removing the DESI-DR1 area. This likelihood is meant to be combined with DESI DR1 or DR2 BAO, but not with future DESI releases.
fiducial_cl	Replace CMB power spectra with fiducial values from data files
flexible_grid	Flexible intrinsic alignment implementation using grid-based bias model
generate_observable_cls	Generate observable C_ell from theoretical power spectra including systematic effects
mass_dependent_ia_model	adds mass dependence to IA, with a linear equation
project_1d	Predict 1pt observable functions to the final n(z) convolved form