Luminosity Distance Reconstruction using an Ensemble of Neural Networks

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Mohammadi Mohammad Hadi; Mehrabi Ahmad

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Journal Paper

Paper Information

Journal: Iranian Journal of Astronomy and Astrophysics Year:2024 | Volume:11 | Issue:2 Page(s): 81-90

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Information Journal Paper

Title

Luminosity Distance Reconstruction using an Ensemble of Neural Networks

Author(s)

Mohammadi Mohammad Hadi | Mehrabi Ahmad | Issue Writer Certificate

Keywords

Model-independent reconstruction

$\Lambda$CDM tension

Abstract

Exploring the consistency of a dataset with the $\Lambda$CDM model across low and high redshifts stands as a compelling topic in cosmology. Given the capability of neural networks to reconstruct an unknown function, we employed an ensemble of neural networks to reconstruct the luminosity distance based on the Pantheon+ dataset. Each network in the ensemble consists of various numbers of layers and neurons. Since the neural network can easily provide a reconstruction with a small value of $\chi2$, it is possible to find a reconstruction with $\chi^2$ smaller than the standard $\Lambda$CDM. We selectively choose those reconstructions with a $\chi^2$ value smaller than the best-fit $\Lambda$CDM model. Our findings reveal that all reconstructions yield a smaller luminosity distance at high redshifts compared to the best $\Lambda$CDM. Assuming a flat universe, we transformed the reconstructions into the Hubble parameter as a function of redshifts and compared the results with predictions of the $\Lambda$CDM model.

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