Hu-Sawicki growth rate fσ8 data

These data were used in the interpretable machine learning analysis in https://github.com/IndiraOcampo/Growth_LSS_model_selection_Lime.git and https://link.aps.org/doi/10.1103/PhysRevLett.134.041002

Aim: Train a Neural Networks (NNs) arquitecture on growth rate fσ8 data to perform model selection between two models: HS and ΛCDM. Consequently, extract information about the influence of each individual fσ8 data point (feature) over the final NNs output.

The current repository contains:

Simulated fσ8 data coming from both models (different sizes of datasets that go from 50 to 5000 realizations) for the Neural Networks analysis.

Data coming from an analysis of 50 data example instances (25 belonging to each model) using Lime, to explore the parameter neighbourhood influence over the output.