Skip to content

Latest commit

 

History

History
56 lines (37 loc) · 1.57 KB

README.md

File metadata and controls

56 lines (37 loc) · 1.57 KB
Raw

snpgm

Originally a hack for the Computing the Universe 2015 workshop. Now a repository for testing out ideas for forward-model/hierarchical supernova cosmology inference.

About

We're performing inference on this Probabalistic Graphical Model (PGM):

PGM

It's specific to the SALT2 light curve model, in terms of the parameters that describe each light curve.

Dependencies:

  • astropy
  • sncosmo
  • emcee
  • triangle
  • daft

... and the usual numpy/scipy/mpl business.

Scripts:

  • gen_pgm.py: Script to draw the PGM. Generates snpgm.png.

  • gen_dataset.py: Generate a test light curve data set, write each light curve to a file in the testdata directory. (In all scripts, directories are created if they don't already exist.)

  • plot_testdata.py: Make a plot of the light curve data for each file in testdata, save to lcplots directory. Just to visualize the light curve data a bit.

  • naive_sampling.py: Throw all the SN parameters and global parameters into a big MCMC and let it run. That's 4*N_SN + 4 parameters.

Importance sampling is two steps:

  • sample_lcs.py: Run an MCMC on each light curve in testdata individually, save samples to samples directory as numpy binary files.

  • importance_sampling.py: Run impotance sampling using the individual SN samples already created in previous step.

Importance sampling papers:

  • Sonnenfeld et al "SL2S Paper 5" (strong lens ensemble)
  • Schneider et al: Hierarchical WL