Dark energy reconstruction analysis with artificial neural networks: Application on simulated Supernova Ia data from Rubin Observatory
This work addresses dark energy reconstruction for cosmology using simulated LSST data, but it is incremental as it applies existing ANN methods to new simulated data.
The paper tackled reconstructing dark energy from simulated Supernova Ia data using an artificial neural network (ANN) with genetic algorithm tuning and Monte Carlo Dropout, showing consistency with theoretical models like ΛCDM and CPL with only minor discrepancies.
In this paper, we present an analysis of Supernova Ia (SNIa) distance moduli $μ(z)$ and dark energy using an Artificial Neural Network (ANN) reconstruction based on LSST simulated three-year SNIa data. The ANNs employed in this study utilize genetic algorithms for hyperparameter tuning and Monte Carlo Dropout for predictions. Our ANN reconstruction architecture is capable of modeling both the distance moduli and their associated statistical errors given redshift values. We compare the performance of the ANN-based reconstruction with two theoretical dark energy models: $Λ$CDM and Chevallier-Linder-Polarski (CPL). Bayesian analysis is conducted for these theoretical models using the LSST simulations and compared with observations from Pantheon and Pantheon+ SNIa real data. We demonstrate that our model-independent ANN reconstruction is consistent with both theoretical models. Performance metrics and statistical tests reveal that the ANN produces distance modulus estimates that align well with the LSST dataset and exhibit only minor discrepancies with $Λ$CDM and CPL.