Observational cosmology with Artificial Neural Networks

TL;DR

This paper introduces artificial neural networks and demonstrates their effectiveness in modeling, classifying, and analyzing cosmological data, offering a promising alternative to traditional methods.

Contribution

It provides an overview of neural networks and showcases their applications in cosmology through three practical examples, highlighting their advantages.

Findings

Neural networks can model cosmological data effectively.

They reduce computational time in numerical tasks.

They are useful for classifying stellar objects.

Abstract

In cosmology, the analysis of observational evidence is very important to test theoretical models of the Universe. Artificial neural networks are powerful and versatile computational tools for data modelling and are recently being considered in the analysis of cosmological data. The main goal of this paper is to provide an introduction to artificial neural networks and to describe some applications to cosmology. We present an overview on the fundamentals of neural networks and their technical details. Throughout three examples, we show their capabilities in modelling cosmological data, saving computational time in numerical tasks, and classifying stellar objects. Artificial neural networks offer interesting qualities that make them a viable alternative method for data analysis in cosmological research.