hierarc.Likelihood.SneLikelihood package

Submodules

hierarc.Likelihood.SneLikelihood.sne_likelihood module

class hierarc.Likelihood.SneLikelihood.sne_likelihood.SneLikelihood(sample_name='CUSTOM', **kwargs_sne_likelihood)[source]

Bases: object

Supernovae likelihood This class supports custom likelihoods as well as likelihoods from the Pantheon sample from file.

log_likelihood(cosmo, apparent_m_z=None, sigma_m_z=None, z_anchor=0.1)[source]
Parameters:

  • cosmo – instance of a class to compute angular diameter distances on arrays

  • apparent_m_z – mean apparent magnitude of SN Ia at z=z_anchor (optional)

  • z_anchor – redshift where definition of apparent_m_z is set (only applicable when apparent_m_z != None)

  • sigma_m_z – 1-sigma scatter in magnitude in the intrinsic SNe brightness distribution not accounted-for by the covariance matrix

Returns:

log likelihood of the data given the specified cosmology

hierarc.Likelihood.SneLikelihood.sne_likelihood_custom module

class hierarc.Likelihood.SneLikelihood.sne_likelihood_custom.CustomSneLikelihood(mag_mean, cov_mag, zhel, zcmb, no_intrinsic_scatter=False)[source]

Bases: object

Class method for an arbitrary apparent magnitude likelihood of a Sne sample where the error and systematic covariance matrix is described in astronomical magnitude space.

log_likelihood_lum_dist(lum_dists, estimated_scriptm=None, sigma_m_z=None)[source]
Parameters:

  • lum_dists – numpy array of luminosity distances to the measured supernovae bins (units do not matter since normalization is subtracted off for the likelihood)

  • estimated_scriptm – mean magnitude at lum_dist=0 (optional)

  • sigma_m_z – 1-sigma scatter in magnitude in the intrinsic SNe brightness distribution not accounted-for by the covariance matrix

Returns:

log likelihood of the data given the luminosity distances

hierarc.Likelihood.SneLikelihood.sne_likelihood_from_file module

This is a lightweight version of the COSMOMC/Cobaya sampler: https://github.com/CobayaSampler/cobaya/blob/71b87842d12c6a04eec182c39b6bef1cd9a987af/cobaya/likelihoods/_base_classes/_sn_prototype.py#L287 It uses the binned Pantheon data: https://github.com/dscolnic/Pantheon/blob/master/Binned_data/lcparam_DS17f.txt And computes the cosmographic likelihood. The main difference is that this class is compatible with the hierArc cosmology module for evaluating likelihoods. This likelihood does NOT include systematics!

  • If you use sn.pantheon, please cite: Scolnic, D. M. et al, 2018 The Complete Light-curve Sample of Spectroscopically Confirmed Type Ia Supernovae from Pan-STARRS1 and Cosmological Constraints from The Combined Pantheon Sample (arXiv:1710.00845)

Synopsis:

Supernovae likelihood, from CosmoMC’s JLA module, for Pantheon and JLA samples.

Author:

Alex Conley, Marc Betoule, Antony Lewis (see source for more specific authorship)

class hierarc.Likelihood.SneLikelihood.sne_likelihood_from_file.SneLikelihoodFromFile(sample_name='Pantheon_binned', pec_z=0.001)[source]

Bases: object

Base likelihood class for evaluating Sne likelihoods.

log_likelihood_lum_dist(lum_dists, estimated_scriptm=None, sigma_m_z=None)[source]
Parameters:

  • lum_dists – numpy array of luminosity distances to the measured supernovae bins (units do not matter since normalization is subtracted off for the likelihood)

  • estimated_scriptm – mean magnitude at lum_dist=0 (optional)

  • sigma_m_z – 1-sigma scatter in magnitude in the intrinsic SNe brightness distribution not accounted-for by the covariance matrix. This variable is not supported in the current implementation of the Pantheon sample

Returns:

log likelihood of the data given the luminosity distances

hierarc.Likelihood.SneLikelihood.sne_likelihood_from_file.read_covariance_matrix(filename, nsn)[source]

Reads in covariance matrix file and returns it as a numpy matrix.

Parameters:

  • filename – string, absolute path of covariance matrix file

  • nsn – number of supernovae (or bins)

Returns:

nxn covariance matrix

hierarc.Likelihood.SneLikelihood.sne_pantheon_plus module

class hierarc.Likelihood.SneLikelihood.sne_pantheon_plus.PantheonPlusData[source]

Bases: object

This class is a lightweight version of the Pantheon+ analysis presented in Pantheon+ likelihood.

The data covariances that are stored in hierArc are originally from Pantheon+ Data products.

If you make use of these products, please cite Brout et al. 2022

build_covariance()[source]

Run once at the start to build the covariance matrix for the data.

Module contents