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

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.