Type Ia Supernovae

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)

This code provides the following likelihoods:

  • sn.pantheon, for the Pantheon SN Ia sample (including Pan-STARRS1 MDS and others)
  • sn.jla, for the JLA SN Ia sample, based on joint SNLS/SDSS SN Ia data
  • sn.jla_lite, an alternative version of sn.jla, marginalized over nuisance parameters

Note

  • If you use sn.pantheon, please cite:
    Scolnic, D. M. et al, 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)
  • If you use sn.jla or sn.jla_lite, please cite:
    Betoule, M. et al, Improved cosmological constraints from a joint analysis of the SDSS-II and SNLS supernova samples (arXiv:1401.4064)

Usage

To use any of these likelihoods, simply mention them in the likelihoods block (do not use sn.jla and its lite version simultaneously), or add them using the input generator.

The settings for each likelihood, as well as the nuisance parameters and their default priors (in the sn.jla case only) can be found in the defaults.yaml files in the folder for the source code of each of these likelihoods, and are reproduced below.

You shouldn’t need to modify any of the options of these likelihoods, but if you really need to, just copy the likelihood block into your input yaml file and modify whatever options you want (you can delete the rest).

# Settings for the Pantheon SN Ia sample, including on Pan-STARRS1 MDS and others.

likelihood:
  sn.pantheon:
    # Path to the data: where the sn_data has been cloned
    path: null
    # .dataset file with settings
    dataset_file: Pantheon/full_long.dataset
    # Overriding of .dataset parameters
    dataset_params:
      # field: value
    # Aliases for automatic covariance matrix
    renames: [Pantheon, Pantheon18]
    # Speed in evaluations/second
    speed: 100
# Settings for JLA supernova sample (joint SNLS/SDSS SN Ia data)
# (For the marginalized version, use 'sn_jla_lite')

likelihood:
  sn.jla:
    # Path to the data: where the sn_data has been cloned
    path: null
    # .dataset file with settings
    dataset_file: JLA/jla.dataset
    # Overriding of .dataset parameters
    dataset_params:
      # field: value
    # Names of alpha and beta parameters if used and varied
    alpha_beta_names: ['alpha_jla', 'beta_jla']
    # Aliases for automatic covariance matrix
    renames: [JLA]
    # Speed in evaluations/second
    speed: 20

params:
  alpha_jla:
    prior:
      min: 0.01
      max: 2
    ref:
      dist: norm
      loc: 0.14
      scale: 0.005
    proposal: 0.005
    latex: \alpha_\mathrm{JLA}
  beta_jla:
    prior:
      min: 0.9
      max: 4.6
    ref:
      dist: norm
      loc: 3.1
      scale: 0.05
    proposal: 0.05
    latex: \beta_\mathrm{JLA}
# Settings for JLA supernova sample (joint SNLS/SDSS SN Ia data)
# Marginalized version (useful e.g. for importance sampling)
# NB: different chi2 normalization from the non-normalized version

likelihood:
  sn.jla_lite:
    # Path to the data: where the sn_data has been cloned
    path: null
    # .dataset file with settings
    dataset_file: JLA/jla.dataset
    # Overriding of .dataset parameters
    dataset_params:
      # field: value
    # Marginalise over nuisance parameters
    # (slow, but useful for importance sampling)
    marginalize: True
    # If marginalizing, pre-compute covariance inverses.
    # Faster, at expense of memory (~600MB).
    precompute_covmats: True
    # Options for the grid marginalization
    marginalize_params:
      marge_steps: 7
      alpha_centre: 0.14
      beta_centre: 3.123
      step_width_alpha: 0.003
      step_width_beta: 0.04
    # Aliases for automatic covariance matrix
    renames: [JLA]
    # Speed in evaluations/second
    speed: 10

Installation

This likelihood can be installed automatically as explained in Installing cosmological codes and data. If are following the instructions there (you should!), you don’t need to read the rest of this section.

Manual installation of the SN Ia likelihoods data

Assuming you are installing all your likelihoods under /path/to/likelihoods, simply do

$ cd /path/to/likelihoods
$ git clone https://github.com/JesusTorrado/sn_data.git

After this, mention the path to this likelihood when you include it in an input file as

likelihood:
  sn.[pantheon|jla|jla_lite]:
    path: /path/to/likelihoods/sn_data