85 citations · 471 across the 19 of their papers we have counts for
19 papers
Comparative analysis of the NANOgrav Hellings-Downs as a window into new physics
Rubén Arjona, Savvas Nesseris, Sachiko Kuroyanagi
Pulsar timing array (PTA) experiments have recently provided strong evidence for the signal of the stochastic gravitational wave background (SGWB) in the nHz-frequency band. These…
Probing CDM through the Weyl potential and machine learning forecasts
Rubén Arjona, Savvas Nesseris, Isaac Tutusaus +2
For years, the cosmological constant and cold dark matter (CDM) model () has stood as a cornerstone in modern cosmology and serves as the predominant theoretical f…
Reconstruction of the swampland conjectures with DESI DR1 BAO data
Rubén Arjona, Savvas Nesseris
The swampland conjectures (SCs) propose constraints on effective field theories that can arise from a consistent theory of quantum gravity. Two prominent SCs suggest that the scala…
A GREAT model comparison against the cosmological constant
Rubén Arjona, Llorenc Espinosa-Portales, Juan García-Bellido +1
Recently, a covariant formulation of non-equilibrium phenomena in the context of General Relativity was proposed in order to explain from first principles the observed accelerated…
Testing the CDM paradigm with growth rate data and machine learning
Rubén Arjona, Alessandro Melchiorri, Savvas Nesseris
The cosmological constant and cold dark matter (CDM) model () is one of the pillars of modern cosmology and is widely used as the de facto theoretical model by cur…
Machine Learning improved fits of the sound horizon at the baryon drag epoch
Andoni Aizpuru, Rubén Arjona, Savvas Nesseris
The baryon acoustic oscillations (BAO) have proven to be an invaluable tool in constraining the expansion history of the Universe at late times and are characterized by the comovin…
Complementary consistency test of the Copernican principle via Noether's theorem and machine learning forecasts
Rubén Arjona, Savvas Nesseris
The Copernican principle (CP), i.e. the assumption that we are not privileged observers of the Universe, is a fundamental tenet of the standard cosmological model. A violation of t…
Novel null tests for the spatial curvature and homogeneity of the Universe and their machine learning reconstructions
Rubén Arjona, Savvas Nesseris
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 cosmolog…
Machine Learning and cosmographic reconstructions of quintessence and the Swampland conjectures
Rubén Arjona, Savvas Nesseris
We present model independent reconstructions of quintessence and the Swampland conjectures (SC) using both Machine Learning (ML) and cosmography. In particular, we demonstrate how…
Cosmological constraints with the Effective Fluid approach for Modified Gravity
Wilmar Cardona, Rubén Arjona, Alejandro Estrada +1
Cosmological constraints of Modified Gravity (MG) models are seldom carried out rigorously. First, even though general MG models evolve differently (i.e., background and perturbati…
Machine learning forecasts of the cosmic distance duality relation with strongly lensed gravitational wave events
Rubén Arjona, Hai-Nan Lin, Savvas Nesseris +1
We use simulated strongly lensed gravitational wave events from the Einstein Telescope to demonstrate how the luminosity and angular diameter distances, and respe…
The effective fluid approach for modified gravity
Rubén Arjona
Current and coming surveys will require sub-percent agreement in theoretical accuracy to test the different cosmological and gravity scenarios. This can be performed with Boltzmann…
Cosmological constraints on non-adiabatic dark energy perturbations
Rubén Arjona, Juan García-Bellido, Savvas Nesseris
The exact nature of dark energy is currently unknown and its cosmological perturbations, when dark energy is assumed not to be the cosmological constant, are usually modeled as adi…
Machine Learning meets the redshift evolution of the CMB Temperature
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 fr…
What can Machine Learning tell us about the background expansion of the Universe?
Rubén Arjona, Savvas Nesseris
Machine learning (ML) algorithms have revolutionized the way we interpret data in astronomy, particle physics, biology and even economics, since they can remove biases due to a pri…
Lensing convergence and anisotropic dark energy in galaxy redshift surveys
Wilmar Cardona, Rubén Arjona, Savvas Nesseris
Analyses of upcoming galaxy surveys will require careful modelling of relevant observables such as the power spectrum of galaxy counts in harmonic space . We investig…
Analytic expressions for the background evolution of massive neutrinos and dark matter particles
Rubén Arjona, Wilmar Cardona, Savvas Nesseris
We provide exact analytic expressions for the density, pressure, average number density and pseudo-pressure for massive neutrinos and generic dark matter particles, both fermions a…
Designing Horndeski and the effective fluid approach
Rubén Arjona, Wilmar Cardona, Savvas Nesseris
We present a family of designer Horndeski models, i.e. models that have a background exactly equal to that of the CDM model but perturbations given by the Horndeski theory. Then…
Unraveling the effective fluid approach for models in the subhorizon approximation
Rubén Arjona, Wilmar Cardona, Savvas Nesseris
We provide explicit formulas for the effective fluid approach of theories, such as the Hu & Sawicki and the designer models. Using the latter and simple modifications to the…