Categorizing models using Self-Organizing Maps: an application to
  modified gravity theories probed by cosmic shear
  We propose to use Self-Organizing Maps (SOM) to map the impact of physical
models onto observables. Using this approach, we are able to determine how
theories relate to each other given their signatures. In cosmology this will be
particularly useful to determine cosmological models (such as dark energy,
modified gravity or inflationary models) that should be tested by the new
generation of experiments. As a first example, we apply this approach to the
representation of a subset of the space of modified gravity theories probed by
cosmic shear. We therefore train a SOM on shear correlation functions in the
f(R), dilaton and symmetron models. The results indicate these three theories
have similar signatures on shear for small values of their parameters but the
dilaton has different signature for higher values. We also show that modified
gravity (especially the dilaton model) has a different impact on cosmic shear
compared to a dynamical dark energy so both need to be tested by galaxy
surveys.
