Interpretability of deep-learning methods applied to large-scale structure surveys

TL;DR

This paper investigates the interpretability of deep learning models in cosmology by analyzing how they utilize different features of large-scale structure data, revealing reliance on both Gaussian and non-Gaussian information.

Contribution

It introduces a novel method to understand CNN decision processes by studying the effects of data degradation on parameter prediction in cosmological surveys.

Findings

The network relies on a mix of Gaussian and non-Gaussian features.

It emphasizes structures at the transition between linear and non-linear scales.

Abstract

Deep learning and convolutional neural networks in particular are powerful and promising tools for cosmological analysis of large-scale structure surveys. They are already providing similar performance to classical analysis methods using fixed summary statistics, are showing potential to break key degeneracies by better probe combination and will likely improve rapidly in the coming years as progress is made in the physical modelling through both software and hardware improvement. One key issue remains: unlike classical analysis, a convolutional neural network's decision process is hidden from the user as the network optimises millions of parameters with no direct physical meaning. This prevents a clear understanding of the potential limitations and biases of the analysis, making it hard to rely on as a main analysis method. In this work, we explore the behaviour of such a convolutional neural network through a novel method. Instead of trying to analyse a network a posteriori, i.e. after training has been completed, we study the impact on the constraining power of training the network and predicting parameters with degraded data where we removed part of the information. This allows us to gain an understanding of which parts and features of a large-scale structure survey are most important in the network's prediction process. We find that the network's prediction process relies on a mix of both Gaussian and non-Gaussian information, and seems to put an emphasis on structures whose scales are at the limit between linear and non-linear regimes.