B0;95;0cThese codes implement a Bayesian Estimation of Multiple Species (BEAMS)-like procedure (Kunz, Bassett & Hlozek 2007) on supernova data. We simultaneously determine Type Ia supernova and core-collapse supernova distances in a sample with a mixture of both.

This implementation was written by David Jones and is used to measure cosmological parameters in:

Jones et al. 2017, ApJ, 843, 6J

Jones et al. 2018, ApJ, 857, 51J

Dependencies

numpy, scipy, emcee (http://dan.iel.fm/emcee/current/user/install/)

There's no real install file yet, just put the BEAMS directory in your PATH.

snbeams.py -h

will provide the rest of the documentation.

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EXAMPLE A:

Run BEAMS on the example data using only Type Ia Supernovae and compare to NN classification. Use just a few steps for this example, but for robust results the nsteps keyword should be at least 1,000. Results are also most robust with the parallel-tempered ensemble sampler (ntemps = 20 works well), but that takes longer to run. First add the repo directory to your $PATH environment variable, and make sure the repo is installed in a directory that is in your PYTHONPATH environment variable. Copy the exampledata directory, the BEAMS.params and mcmcparams.input files to your local directory and then run:

snbeams.py -p BEAMS.params -f exampledata/PS1_SIM_example.FITRES -o exampledata/PS1_BEAMS.SNIa.out --onlyIa --piacol PTRUE_Ia --ninit 50 --nsteps 200

snbeams.py -p BEAMS.params -f exampledata/PS1_SIM_example.FITRES -o exampledata/PS1_BEAMS.NN.out --piacol PNN_Ia --ninit 50 --nsteps 200

BEAMS then produces 3 files, using the fileroot of the output file you specify: the basic output file, a file in FITRES format, where the SN Ia distances have been converted to mB/mBERR values (distance mod. - 19.36), and a distance modulus covariance matrix.

in python:

from astropy.cosmology import FlatLambdaCDM
cosmo=FlatLambdaCDM(H0=70, Om0=0.3)
from BEAMS.txtobj import txtobj
true = txtobj('exampledata/PS1_BEAMS.SNIa.out')
nn = txtobj('exampledata/PS1_BEAMS.NN.out')

plt.errorbar(true.zCMB,true.popAmean-cosmo.distmod(true.zCMB).value,
             yerr=true.popAmean_err,fmt='o',label='true SN Ia distances')
plt.errorbar(nn.zCMB,true.popAmean-cosmo.distmod(nn.zCMB).value,
             yerr=nn.popAmean_err,fmt='o',label='SN Ia distances in a sample with CC SNe')
plt.ylim([-0.1,0.1])
plt.xlim([0.01,0.7])
plt.xscale('log')
plt.xlabel('$z$')
plt.ylabel('Hubble Residual')
plt.legend()

the results look great! Unfortunately, they don't mean anything yet. You really need to use the parallel-tempered ensemble sampler - or turn off the scale and shift parameters in mcmc.params - and use many more steps if you want robust results.