Probabilistic Inference of Cosmological Density Parameters from Synthetic Hubble Expansion Data of Varying SNR Using Diverse Artificial Neural Network Architectures

Zijian Jin, Jaehyon Rhee

This paper builds upon ParamANN's novel approach (S. Pal & R. Saha 2024) of using ANNs to infer cosmological density parameters by determining optimal architecture for varying synt…