Clustering and weak lensing from DES Y1


DES likelihood, independent Python implementation. Well tested and agrees with likelihoods in DES chains for fixed nu mass.


Antony Lewis (little changes for Cobaya by Jesus Torrado)


If you use any of these likelihoods, please cite them as:
Abbott, T. M. C. and others, Dark Energy Survey year 1 results: Cosmological constraints from galaxy clustering and weak lensing (arXiv:1708.01530)

Likelihoods of the DES Y1 data release, described in the paper mentioned above:

  • des_y1.clustering

  • des_y1.shear

  • des_y1.galaxy_galaxy

  • des_y1.joint (a shortcut for the combination of the previous three)


To use any of the DES likelihoods, you simply need to mention them in the likelihood block, or add them using the input generator.

The corresponding nuisance parameters will be added automatically, so you don’t have to care about listing them in the params block.

The nuisance parameters and their default priors can be obtained as explained in Getting help and bibliography for a component.


This likelihood can be installed automatically as explained in Installing cosmological codes and data.