Dark energy reconstruction analysis with artificial neural networks: Application on simulated Supernova Ia data from Rubin Observatory
Mitra A. Gómez-Vargas I. Zarikas V.
December 2024Elsevier B.V.
Physics of the Dark Universe
2024#46
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: ΛCDM 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 ΛCDM and CPL.
Dark energy , Data analysis , Machine Learning , Supernovae
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Center for AstroPhysical Surveys, National Center for Supercomputing Applications, University of Illinois Urbana-Champaign, Urbana, 61801, IL, United States
Department of Astronomy, University of Illinois at Urbana-Champaign, Urbana, 61801, IL, United States
Kazakh-British Technical University, 59 Tole Bi Street, Almaty, 050000, Kazakhstan
Instituto de Ciencias Físicas, Universidad Nacional Autónoma de México, Morelos, Cuernavaca, 62210, Mexico
Department of Astronomy of the University of Geneva, 51 Chemin Pegasi, Versoix, 1290, Switzerland
Department of Mathematics, University of Thessaly, Lamia, 35132, Greece
Center for AstroPhysical Surveys
Department of Astronomy
Kazakh-British Technical University
Instituto de Ciencias Físicas
Department of Astronomy of the University of Geneva
Department of Mathematics
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