Search Results for author: Rubén Arjona

Found 5 papers, 2 papers with code

Novel null tests for the spatial curvature and homogeneity of the Universe and their machine learning reconstructions

no code implementations11 Mar 2021 Rubén Arjona, Savvas Nesseris

Second, we present a new test of possible deviations from homogeneity using the combination of two datasets, either the baryon acoustic oscillation (BAO) and $H(z)$ data or the transversal and radial BAO data, while we also introduce two consistency tests for $\Lambda$CDM which could be reconstructed via the transversal and radial BAO data.

Cosmology and Nongalactic Astrophysics General Relativity and Quantum Cosmology High Energy Physics - Phenomenology

Machine Learning and cosmographic reconstructions of quintessence and the Swampland conjectures

no code implementations22 Dec 2020 Rubén Arjona, Savvas Nesseris

Using the Hubble parameter $H(z)$ data from the cosmic chronometers we find that the ML and cosmography reconstructions of the SC are compatible with observations at low redshifts.

Cosmology and Nongalactic Astrophysics General Relativity and Quantum Cosmology High Energy Physics - Phenomenology

Machine learning forecasts of the cosmic distance duality relation with strongly lensed gravitational wave events

no code implementations5 Nov 2020 Rubén Arjona, Hai-Nan Lin, Savvas Nesseris, Li Tang

We use simulated strongly lensed gravitational wave events from the Einstein Telescope to demonstrate how the luminosity and angular diameter distances, $d_L(z)$ and $d_A(z)$ respectively, can be combined to test in a model independent manner for deviations from the cosmic distance duality relation and the standard cosmological model.

Cosmology and Nongalactic Astrophysics General Relativity and Quantum Cosmology High Energy Physics - Phenomenology

Machine Learning meets the redshift evolution of the CMB Temperature

2 code implementations28 Feb 2020 Rubén Arjona

We present a model independent and non-parametric reconstruction with a Machine Learning algorithm of the redshift evolution of the Cosmic Microwave Background (CMB) temperature from a wide redshift range $z\in \left[0, 3\right]$ without assuming any dark energy model, an adiabatic universe or photon number conservation.

Cosmology and Nongalactic Astrophysics General Relativity and Quantum Cosmology High Energy Physics - Phenomenology

What can Machine Learning tell us about the background expansion of the Universe?

2 code implementations3 Oct 2019 Rubén Arjona, Savvas Nesseris

We also confirm a recently reported mild tension between the SnIa/quasar data and the cosmological constant $\Lambda$CDM model at high redshifts $(z\gtrsim1. 5)$ and finally, we show that the GA can be used in complementary null tests of the $\Lambda$CDM via reconstructions of the Hubble parameter and the luminosity distance.

Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics General Relativity and Quantum Cosmology

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