Data compression in cosmology: A compressed likelihood for Planck data
  We apply the massively optimized parameter estimation and data compression
technique (MOPED) to the public Planck 2015 temperature likelihood, reducing
the dimensions of the data space to one number per parameter of interest. We
present CosMOPED, a lightweight and convenient compressed likelihood code
implemented in Python. In doing so we show that the $\ell<30$ Planck
temperature likelihood can be well approximated by two Gaussian distributed
data points, which allows us to replace the map-based low-$\ell$ temperature
likelihood by a simple Gaussian likelihood. We make available a Python
implementation of Planck's 2015 Plik_lite temperature likelihood that includes
these low-$\ell$ binned temperature data (Planck-lite-py). We do not explicitly
use the large-scale polarization data in CosMOPED, instead imposing a prior on
the optical depth to reionization derived from these data. We show that the
$\Lambda$CDM parameters recovered with CosMOPED are consistent with the
uncompressed likelihood to within 0.1$\sigma$, and test that a 7-parameter
extended model performs similarly well.
