2023/02/24 by Lorenzo Giambagli, Giambagli, Lorenzo, Duccio Fanelli +5 · 1 citation
Mathematics · Physics and Astronomy · #Cosmology and Nongalactic Astrophysics (astro-ph.CO) #Disordered Systems and Neural Networks (cond-mat.dis-nn) #FOS: Physical sciences #Gamma-ray bursts and supernovae #Instrumentation and Methods for Astrophysics (astro-ph.IM) #Statistical and numerical algorithms #Stellar, planetary, and galactic studies
paper · pdf · doi:10.48550/arxiv.2302.12582
openalex publication_date 2023/02/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The recent extension of the Hubble diagram of Supernovae and quasars to redshifts much higher than 1 prompted a revived interest in non-parametric approaches to test cosmological models and to measure the expansion rate of the Universe. In particular, it is of great interest to infer model-independent constraints on the possible evolution of the dark energy component. Here we present a new method, based on a Neural Network Regression, to analyze the Hubble Diagram in a completely non-parametric, model-independent fashion. We first validate the method through simulated samples with the same redshift distribution as the real ones, and discuss the limitations related to the "inversion problem" for the distance-redshift relation. We then apply this new technique to the analysis of the Hubble diagram of Supernovae and quasars. We confirm that the data up to z ∼ 1-1.5 are in agreement with a flat ΛCDM model with ΩM ∼ 0.3, while ∼ 5-sigma deviations emerge at higher redshifts. A flat ΛCDM model would still be compatible with the data with ΩM > 0.4. Allowing for a generic evolution of the dark energy component, we find solutions suggesting an increasing value of ΩM with the redshift, as predicted by interacting dark sector models.