Novel null tests for the spatial curvature and homogeneity of the Universe and their machine learning reconstructions
Rub\'en Arjona, Savvas Nesseris

TL;DR
This paper introduces new consistency tests for the standard cosmological model using observational data, and employs machine learning to reconstruct these tests, revealing a significant tension in the transition redshift.
Contribution
The paper presents novel, model-independent consistency tests for spatial curvature and homogeneity, and applies machine learning for their reconstruction from observational data.
Findings
A $ ext{~}4σ$ tension on the transition redshift from $H(z)$ and BAO data.
New tests for deviations from $ ext{ΛCDM}$ using $H(z)$ and BAO data.
Machine learning reconstruction of cosmological consistency tests.
Abstract
A plethora of observational data obtained over the last couple of decades has allowed cosmology to enter into a precision era and has led to the foundation of the standard cosmological constant and cold dark matter paradigm, known as the CDM model. Given the many possible extensions of this concordance model, we present here several novel consistency tests which could be used to probe for deviations from CDM. First, we derive a joint consistency test for the spatial curvature and the matter density parameters, constructed using only the Hubble rate , which can be determined directly from observations. Second, we present a new test of possible deviations from homogeneity using the combination of two datasets, either the baryon acoustic oscillation (BAO) and data or the transversal and radial BAO data, while we also…
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