Categorizing models using Self-Organizing Maps: an application to modified gravity theories probed by cosmic shear

TL;DR

This paper introduces a method using Self-Organizing Maps to categorize and compare cosmological models based on their observable signatures in cosmic shear data, aiding model testing with upcoming experiments.

Contribution

It applies Self-Organizing Maps to classify modified gravity theories by their cosmic shear signatures, providing a new tool for cosmological model differentiation.

Findings

The SOM can distinguish between different modified gravity models.

Similar signatures for small parameters in different theories.

Modified gravity impacts cosmic shear differently than dark energy.

Abstract

We propose to use Self-Organizing Maps (SOM) to map the impact of physical models onto observables. Using this approach, we are be 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.