Extreme data compression while searching for new physics
  Bringing a high-dimensional dataset into science-ready shape is a formidable
challenge that often necessitates data compression. Compression has accordingly
become a key consideration for contemporary cosmology, affecting public data
releases, and reanalyses searching for new physics. However, data compression
optimized for a particular model can suppress signs of new physics, or even
remove them altogether. We therefore provide a solution for exploring new
physics \emph{during} data compression. In particular, we store additional
agnostic compressed data points, selected to enable precise constraints of
non-standard physics at a later date. Our procedure is based on the maximal
compression of the MOPED algorithm, which optimally filters the data with
respect to a baseline model. We select additional filters, based on a
generalised principal component analysis, which are carefully constructed to
scout for new physics at high precision and speed. We refer to the augmented
set of filters as MOPED-PC. They enable an analytic computation of Bayesian
evidences that may indicate the presence of new physics, and fast analytic
estimates of best-fitting parameters when adopting a specific non-standard
theory, without further expensive MCMC analysis. As there may be large numbers
of non-standard theories, the speed of the method becomes essential. Should no
new physics be found, then our approach preserves the precision of the standard
parameters. As a result, we achieve very rapid and maximally precise
constraints of standard and non-standard physics, with a technique that scales
well to large dimensional datasets.
