Testing $\Lambda$CDM with ANN-Reconstructed Expansion History from Cosmic Chronometers

TL;DR

This paper introduces an ANN-based non-parametric method to reconstruct the Universe's expansion history from cosmic chronometer data, confirming consistency with the ΛCDM model.

Contribution

It presents a novel three-stage ANN framework for model-independent cosmic expansion reconstruction validated on real data.

Findings

Reconstructed H(z) aligns with ΛCDM predictions within uncertainties.

The framework is robust and reliable for analyzing cosmic expansion.

Demonstrates effectiveness of ANN in cosmological data analysis.

Abstract

In modern cosmology, the rapid growth of high-precision observational data, along with significant theoretical advances, has intensified the challenge of identifying a robust, model-independent framework to probe the expansion history of the Universe. In this work, we propose a novel artificial neural network (ANN)-based framework for the non-parametric reconstruction of the late-time cosmic expansion. The framework is trained and validated through a three-stage screening pipeline prior to its application to real observational data. As a demonstration of its effectiveness, we reconstruct the Hubble parameter $H(z)$ using the latest cosmic chronometer measurements. Our results show that the reconstructed expansion history aligns with the predictions of the $\Lambda$CDM model within observational uncertainties, thereby supporting the robustness and reliability of the proposed approach.