Code used in arXiv:1707.00007 ("Clarifying the Hubble constant tension with a Bayesian hierarchical model of the local distance ladder") and arXiv:1802.03404 ("Prospects for resolving the Hubble constant tension with standard sirens").
Authors
- Stephen Feeney, Daniel Mortlock and Niccolò Dalmasso
- Stephen Feeney, Hiranya Peiris, Andrew Williamson, Samaya Nissanke, Daniel Mortlock, Justin Alsing and Dan Scolnic
Clarifying the Hubble constant tension with a Bayesian hierarchical model of the local distance ladder
A more complete README is incoming, as is a more user-friendly interface. For now, note the following dependencies
and that the Planck chains can be downloaded from the Planck Legacy Archive for comparison. Why not try the following?
python stan_cpl.py
python stan_cpl_sample_plots.py
The following variables can be changed in stan_cpl.py to set up different runs. Any changes should also be made to stan_cpl_sample_plots.py to process the resulting outputs.
n_chains = 4 # number of independent parallel chains
n_samples = 10000 # 100000 will take 10-20 hours (linear scaling)
recompile = True # True recompiles Stan model: set to False after first run
use_riess_rejection = False # sigma clip Cepheids before passing to BHM
sne_sum = False # replace full SN dataset with intercept of mag-log(z) relation
gauss_mu_like = False # sample from anchor distance moduli, not distances
model_outliers = None # Gauss (None) or heavy-tailed ('ht') intrinsic scatter
ng_maser_pdf = False # replace Gauss MASER distance likelihood with (approx) non-Gauss form
nir_sne = False # use near infra-red SNe from Dhawan et al. (1707.00715)
fit_cosmo_delta = None # fit H_0 (None) or perform model selection ('hq')
v_pla = 2015 # use Planck 2015 or 2016 inputs in model selection
constrain = True # if using simulated data, fix random seed to test stability
stan_constrain = True # fix random seed in sampling run
setup = 'r16' # dataset: try 'r16' (1604.01424) or 'd17' (1707.00715)
sim = True # fit existing data or simulationNote the following dependencies:
Try python h_of_z.py to produce inverse distance ladder constraints, and python gw_grb_h_0_ppd.py (or mpirun -np X python gw_grb_h_0_ppd.py if you have MPI4PY installed and want to use it) to generate binary neutron star merger constraints.
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