Observational cosmology with Artificial Neural Networks

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.

17 pages, 13 figures; matches the version published in Universe

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• Non-parametric reconstruction of cosmological observables using Gaussian Processes Regression
• LADDER: Revisiting the Cosmic Distance Ladder with Deep Learning Approaches and Exploring its Applications
• Detection of Dipole Modulation in CMB Temperature Anisotropy Maps from WMAP and Planck using Artificial Intelligence
• Deep Learning and genetic algorithms for cosmological Bayesian inference speed-up
• Analysis of Dark Matter Halo Structure Formation in $N$-body Simulations with Machine Learning
• ParamANN: A Neural Network to Estimate Cosmological Parameters for $\Lambda$CDM Universe Using Hubble Measurements
• Reconstruction of full sky CMB $\mathbf{E}$ and $\mathbf{B}$ modes spectra removing $\mathbf{E}$-to-$\mathbf{B}$ leakage from partial sky using deep learning
• In Search of Global 21-cm Signal using Artificial Neural Network in light of ARCADE 2
• Late-Time Cosmic Acceleration in Ricci-Gauss-Bonnet Gravity via Gradient Descent Optimization