Low redshift observational constraints on dark energy models using ANN - CosmicANNEstimator

TL;DR

This paper introduces CosmicANNEstimator, a neural network-based method for estimating cosmological parameters from observational data, offering a faster alternative to traditional MCMC techniques within the $\\Lambda$CDM model.

Contribution

It develops a machine learning framework using neural networks trained on synthetic data to efficiently constrain cosmological parameters from observational datasets.

Findings

Neural network estimates are comparable to MCMC results.

The method effectively incorporates observational uncertainties.

CosmicANNEstimator accelerates parameter inference in cosmology.

Abstract

We present CosmicANNEstimator (Cosmological Parameters Artificial Neural Network Estimator), a machine learning approach for constraining cosmological parameters within the Lambda Cold Dark Matter ($\Lambda$CDM) framework. Our methodology employs two specialized artificial neural networks (ANNs) designed to analyze Hubble parameter and Supernova data independently. The estimator is trained on synthetic data covering broad parameter ranges, with Gaussian random noise incorporated to simulate observational uncertainties. Our results demonstrate parameter estimates and associated uncertainties comparable to traditional Markov Chain Monte Carlo (MCMC) methods, establishing machine learning as an efficient alternative for cosmological parameter estimation. This work underscores the potential of neural network-based inference to complement traditional Bayesian methods and accelerate future cosmological analyses.