arXiv subjects

Rubén Arjona

Publications and source records attributed to Rubén Arjona.

At least 19 recordsLinked to original sources

arXiv2025

Reconstruction of the swampland conjectures with DESI DR1 BAO data

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 scalar field excursion and the gradient of the potential should be at most $\mathcal{O}(1)$ in Planck units. Using the first data release from the Dark Energy Spectroscopic Instrument (DESI) survey and model-independent reconstructions of the SC-related quantities at late times, via a machine learning approach known as genetic algorithms, we evaluate the consistency of these reconstructions with the SC expectations. Our results indicate that the reconstructed second SC is several sigmas away from zero, suggesting a steep potential, in contrast to recent model-specific analyses assuming exponential potentials. The novelty of our approach lies in using solely model-independent reconstructions of cosmological observables from DESI, such as the angular diameter distance and the Hubble expansion history. This makes our method readily applicable to forthcoming data from stage IV surveys, providing a framework for further assessing consistency with SCs.

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arXiv2024

Comparative analysis of the NANOgrav Hellings-Downs as a window into new physics

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 experiments have shown a statistical preference for the Hellings-Downs (HD) correlation between pulsars, which is widely regarded as a definitive signature of the SGWB. Using the NANOGrav 15-year dataset, we perform a comparative Bayesian analysis of four different models that go beyond the standard cosmological framework and influence the overlap reduction function. Specifically, we analyze ultralight vector dark matter (DM), spin-2 ultralight DM, massive gravity, and a folded non-Gaussian component to the SGWB. We find that the spin-2 ultralight DM and the massive gravity model are statistically equivalent to the HD prediction, and there is weak evidence in favor of the non-Gaussian component and the ultralight vector DM model. We also perform a non-parametric test using the Genetic Algorithms, which suggests a weak deviation from the HD curve. However, improved data quality is required before drawing definitive conclusions.

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arXiv2024

Probing $Λ$CDM through the Weyl potential and machine learning forecasts

For years, the cosmological constant $Λ$ and cold dark matter (CDM) model ($Λ\text{CDM}$) has stood as a cornerstone in modern cosmology and serves as the predominant theoretical framework for current and forthcoming surveys. However, the latest results shown by the Dark Energy Spectroscopic Instrument (DESI), along other cosmological data, show hints in favor of an evolving dark energy. Given the elusive nature of dark energy and the imperative to circumvent model bias, we introduce a novel null test, derived from Noether's theorem, that uses measurements of the Weyl potential (the sum of the spatial and temporal distortion) at different redshifts. In order to assess the consistency of the concordance model we quantify the precision of this null test through the reconstruction of mock catalogs based on $Λ\text{CDM}$ using forthcoming survey data, employing Genetic Algorithms, a machine learning technique. Our analysis indicates that with forthcoming LSST-like and DESI-like mock data our consistency test will be able to rule out several cosmological models at around 4$σ$ and help to check for tensions in the data.

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arXiv2024

Lensing convergence and anisotropic dark energy in galaxy redshift surveys

Analyses of upcoming galaxy surveys will require careful modelling of relevant observables such as the power spectrum of galaxy counts in harmonic space $C_\ell(z,z')$. We investigate the impact of disregarding relevant relativistic effects by considering a model of dark energy including constant sound speed $c_{\rm eff}^2$, constant equation of state $w$, and anisotropic stress sourced by matter perturbations $π$. Cosmological constraints were computed using cosmic microwave background anisotropies, baryon acoustic oscillations, supernovae type Ia, and redshift space distortions. Our results are consistent with $w=-1$, $c_{\rm eff}^2=1$, and $π=0$. Then, a forecast for the performance of an Euclid-like galaxy survey was carried out also adding information from other probes. Here we show that, regardless of the galaxy survey configuration, neglecting the effect of lensing convergence will lead to substantial shifts in the galaxy bias $b_0$ and the neutrino mass $\sum m_ν$. Shifts in the dark energy sound speed and anisotropic stress also appear, but they depend on the survey configuration and hence lack robustness. While neglecting lensing convergence also leads to a Hubble constant $H_0$ moving downwards, the significance of the shift is not big enough to play a relevant part in the current $H_0$ tension.

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arXiv2022

A GREAT model comparison against the cosmological constant

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 expansion of the Universe, without the need for a cosmological constant, leading to the GREA theory. Here, we confront the GREA theory against the latest cosmological data, including type Ia supernovae, baryon acoustic oscillations, the cosmic microwave background (CMB) radiation, Hubble rate data from the cosmic chronometers and the recent $H_0$ measurements. We perform Markov Chain Monte Carlo analyses and a Bayesian model comparison, by estimating the evidence via thermodynamic integration, and find that when all the aforementioned data are included, but no prior on $H_0$, the difference in the log-evidence is $\sim -9$ in favor of GREA, thus resulting in overwhelming support for the latter over the cosmological constant and cold dark matter model ($Λ$CDM). When we also include priors on $H_0$, either from Cepheids or the Tip of the Red Giant Branch measurements, then due to the tensions with CMB data the GREA theory is found to be statistically equivalent with $Λ$CDM.

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arXiv2021

Complementary consistency test of the Copernican principle via Noether's theorem and machine learning forecasts

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 this postulate implies the possibility that the apparent cosmic acceleration could be explained without the need of a cosmological constant, dark energy or paper we present a new test of the CP relating the distance and the expansion rate, derived via Noether's theorem, which is complementary to other tests found in the literature. We also simulate fiducial data based on upcoming stage IV galaxy surveys and use them to reconstruct the Hubble rate $H(z)$ and the angular diameter distance $d_A(z)$ in order to forecast how well our null test can constrain deviations from the cosmological constant model. We find that our new test can easily rule out several scenarios based on the Lema\^ıtre-Tolman-Bondi void model at confidence of $\gtrsim 3σ$ at middle to high redshifts ($z>0.5$).

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arXiv2021

Machine Learning improved fits of the sound horizon at the baryon drag epoch

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 comoving sound horizon at the baryon drag epoch $r_\mathrm{s}(z_\mathrm{d})$. The latter quantity can be calculated either numerically using recombination codes or via fitting functions, such as the one by Eisenstein and Hu (EH), made via grids of parameters of the recombination history. Here we quantify the accuracy of these expressions and show that they can strongly bias the derived constraints on the cosmological parameters using BAO data. Then, using a machine learning approach, called the genetic algorithms, we proceed to derive new analytic expressions for $r_\mathrm{s}(z_\mathrm{d})$ which are accurate at the $\sim0.003\%$ level in a range of $10σ$ around the Planck 2018 best-fit or $\sim0.018\%$ in a much broader range, compared to $\sim 2-4\%$ for the EH expression, thus obtaining an improvement of two to three orders of magnitude. Moreover, we also provide fits that include the effects of massive neutrinos and an extension to the concordance cosmological model assuming variations of the fine structure constant. Finally, we note that our expressions can be used to ease the computational cost required to compute $r_\mathrm{s}(z_\mathrm{d})$ with a Boltzmann code when deriving cosmological constraints using BAO data from current and upcoming surveys.

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arXiv2021

Testing the $Λ$CDM paradigm with growth rate data and machine learning

The cosmological constant $Λ$ and cold dark matter (CDM) model ($Λ\text{CDM}$) is one of the pillars of modern cosmology and is widely used as the de facto theoretical model by current and forthcoming surveys. As the nature of dark energy is very elusive, in order to avoid the problem of model bias, here we present a novel null test at the perturbation level that uses the growth of matter perturbation data in order to assess the concordance model. We analyze how accurate this null test can be reconstructed by using data from forthcoming surveys creating mock catalogs based on $Λ\text{CDM}$ and three models that display a different evolution of the matter perturbations, namely a dark energy model with constant equation of state $w$ ($w$CDM), the Hu \& Sawicki and designer $f(R)$ models, and we reconstruct them with a machine learning technique known as the Genetic Algorithms. We show that with future LSST-like mock data our consistency test will be able to rule out these viable cosmological models at more than 5$σ$, help to check for tensions in the data and alleviate the existing tension of the amplitude of matter fluctuations $S_8=σ_8\left(Ω_m/0.3\right)^{0.5}$.

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arXiv2021

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

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 $Λ$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 $Λ$CDM. First, we derive a joint consistency test for the spatial curvature $Ω_{k,0}$ and the matter density $Ω_\textrm{m,0}$ parameters, constructed using only the Hubble rate $H(z)$, 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 $H(z)$ data or the transversal and radial BAO data, while we also introduce two consistency tests for $Λ$CDM which could be reconstructed via the transversal and radial BAO data. We then reconstruct the aforementioned tests using the currently available data in a model independent manner using a particular machine learning approach, namely the Genetic Algorithms. Finally, we also report on a $\sim 4σ$ tension on the transition redshift as determined by the $H(z)$ and radial BAO data.

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arXiv2021

Cosmological constraints with the Effective Fluid approach for Modified Gravity

Cosmological constraints of Modified Gravity (MG) models are seldom carried out rigorously. First, even though general MG models evolve differently (i.e., background and perturbations) to the standard cosmological model, it is usual to assume a $Λ$CDM background. This treatment is not correct and in the era of precision cosmology could induce undesired biases in cosmological parameters. Second, neutrino mass is usually held fixed in the analyses which could obscure its relation to MG parameters. In a couple of previous papers we showed that by using the Effective Fluid Approach we can accurately compute observables in fairly general MG models. An appealing advantage of our approach is that it allows a pretty easy implementation of this kinds of models in Boltzmann solvers (i.e., less error--prone) while having a useful analytical description of the effective fluid to understand the underlying physics. This paper illustrates how an effective fluid approach can be used to carry out proper analyses of cosmological constraints in MG models. We investigated three MG models including the sum of neutrino masses as a varying parameter in our Markov Chain Monte Carlo analyses. Two models (i.e., Designer $f(R)$ [DES-fR] and Designer Horndeski [HDES]) have a background matching $Λ$CDM, while in a third model (i.e., Hu $\&$ Sawicki $f(R)$ model [HS]) the background differs from the standard model. In this way we estimate how relevant the background is when constraining MG parameters along with neutrinos' masses. We implement the models in the popular Boltzmann solver CLASS and use recent, available data (i.e., Planck 2018, CMB lensing, BAO, SNIa Pantheon compilation, $H_0$ from SHOES, and RSD Gold-18 compilation) to compute tight cosmological constraints in the MG parameters that account for deviation from the $Λ$CDM model. [abridged]

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arXiv2021

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

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. In particular, we use two machine learning approaches, the Genetic Algorithms and Gaussian Processes, to reconstruct the mock data and we show that both approaches are capable of correctly recovering the underlying fiducial model and can provide percent-level constraints at intermediate redshifts when applied to future Einstein Telescope data.

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arXiv2021

Machine Learning and cosmographic reconstructions of quintessence and the Swampland conjectures

We present model independent reconstructions of quintessence and the Swampland conjectures (SC) using both Machine Learning (ML) and cosmography. In particular, we demonstrate how the synergies between theoretical analyses and ML can provide key insights on the nature of dark energy and modified gravity. 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. Finally, including the growth rate data $fσ_8(z)$ we perform a model independent test of modified gravity cosmologies through two phase diagrams, namely $H-fσ_8$ and $η-fσ_8$, where the anisotropic stress parameter $η$ is obtained via the $E_g$ statistics, which is related to gravitational lensing data. While the first diagram is consistent within the errors with the $Λ$CDM model, the second one has a $\sim 2σ$ deviation of the anisotropic stress from unity at $z\sim 0.3$ and a $\sim 4σ$ deviation at $z\sim 0.9$, thus pointing toward mild deviations from General Relativity, which could be further tested with upcoming large-scale structure surveys.

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arXiv2020

Hints of dark energy anisotropic stress using Machine Learning

Recent analyses of the Planck data and quasars at high redshifts have suggested possible deviations from the flat $Λ$ cold dark matter model ($Λ$CDM), where $Λ$ is the cosmological constant. Here we use machine learning methods to investigate any possible deviations from $Λ$CDM at both low and high redshifts by using the latest cosmological data. Specifically, we apply the Genetic Algorithms to explore the nature of dark energy (DE) in a model independent fashion by reconstructing its equation of state $w(z)$, the growth index of matter density perturbations $γ(z)$, the linear DE anisotropic stress $η_\textrm{DE}(z)$ and the adiabatic sound speed $c_\textrm{s,DE}^2(z)$ of DE perturbations. We find a $\sim2σ$ deviation of $w(z)$ from -1 at high redshifts, the adiabatic sound speed is negative at the $\sim2.5σ$ level at $z=0.1$ and a $\sim2σ$ deviation of the anisotropic stress from unity at low redshifts and $\sim4 σ$ at high redshifts. These results hint towards either the presence of an non-adiabatic component in the DE sound speed or the presence of DE anisotropic stress, thus hinting at possible deviations from the $Λ$CDM model.

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arXiv2020

Cosmological constraints on non-adiabatic dark energy perturbations

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 adiabatic. Here we explore the possibility that dark energy might have a nonadiabatic component and we examine how it would affect several key cosmological observables. We present analytical solutions for the growth rate and growth index of matter density perturbations and compare them to both numerical solutions of the fluid equations and an implementation in the Boltzmann code CLASS, finding that they all agree to well below one percent. We also perform a Monte Carlo analysis to derive constraints on the parameters of the nonadiabatic component using the latest cosmological data, including the temperature and polarization spectra of the Cosmic Microwave Background as observed by Planck, the Baryon Acoustic Oscillations, the Pantheon type Ia supernovae compilation and lastly, measurements of Redshift Space Distortions (RSD) of the growth rate of matter perturbations. We find that the amplitude of the nonadiabatic pressure perturbation is consistent with zero within $1σ$. Finally, we also present a new, publicly available, RSD likelihood for MontePython based on the "Gold 2018" growth rate data compilation.

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arXiv2020

The effective fluid approach for modified gravity

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 solvers, i.e. codes that solve the linear evolution of cosmological perturbations. Given the plethora of gravity models, it is crucial to have a standardized unified way to describe all of them and take them into account in a Boltzmann code. Dark Energy (DE) and Modified Gravity (MG) models, although at a first glance quite dissimilar, are possible to unify them within the same framework. In this paper we present a scenario, based on the effective fluid approach that allows to map any modified gravity model as an effective dark energy fluid and then we show how to implement it into existing Boltzmann codes in a simple and straightforward way. This approach has also the advantage that only a handful of variables are needed to compute, i.e the equation of state $w(a)$ at the background level and the sound speed $c_s^2(a,k)$ and the anisotropic stress $π(a,k)$ at the linear perturbation. In particular we show that with simple modifications to the latter Cosmic Linear Anisotropy Solving System (CLASS) code, which we called EFCLASS, we provide competitive results in a much simpler and less error-prone approach in including the effects of modified gravity models. To test our modifications, we particularize the effective fluid approach to f(R) theories and a surviving class of Horndeski models, the designer Horndeski (HDES) which have a background exactly to the standard cosmological model $Λ$CDM.

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arXiv2020

Machine Learning meets the redshift evolution of the CMB Temperature

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. In particular we use the genetic algorithms which avoid the dependency on an initial prior or a cosmological fiducial model. Through our reconstruction we constrain new physics at late times. We provide novel and updated estimates on the $β$ parameter from the parametrisation $\text{T}(z)=\text{T}_0(1+z)^{1-β}$, the duality relation $η(z)$ and the cosmic opacity parameter $τ(z)$. Furthermore we place constraints on a temporal varying fine structure constant $α$, which would have signatures in a broad spectrum of physical phenomena such as the CMB anisotropies. Overall we find no evidence of deviations within the $1σ$ region from the well established $Λ\text{CDM}$ model, thus confirming its predictive potential.

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arXiv2020

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

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 priori chosen models. Here we apply a particular ML method, the genetic algorithms (GA), to cosmological data that describes the background expansion of the Universe, namely the Pantheon Type Ia supernovae and the Hubble expansion history $H(z)$ datasets. We obtain model independent and nonparametric reconstructions of the luminosity distance $d_L(z)$ and Hubble parameter $H(z)$ without assuming any dark energy model or a flat Universe. We then estimate the deceleration parameter $q(z)$, a measure of the acceleration of the Universe, and we make a $\sim4.5σ$ model independent detection of the accelerated expansion, but we also place constraints on the transition redshift of the acceleration phase $(z_{\textrm{tr}}=0.662\pm0.027)$. We also find a deviation from $Λ$CDM at high redshifts, albeit within the errors, hinting toward the recently alleged tension between the SnIa/quasar data and the cosmological constant $Λ$CDM model at high redshifts $(z\gtrsim1.5)$. Finally, we show the GA can be used in complementary null tests of the $Λ$CDM via reconstructions of the Hubble parameter and the luminosity distance.

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arXiv2019

Analytic expressions for the background evolution of massive neutrinos and dark matter particles

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 and bosons. We then focus on massive neutrinos and we compare our analytic expressions with the numerical implementation in the CLASS Boltzmann code. We find that our modifications including the exact analytic expressions are in agreement to better than $10^{-4}\%$ with the default CLASS implementation in the estimation of the CMB power spectrum; our modifications do not have an impact on the performance of the code. We also provide several specific limits of our expressions at the relativistic regime, but also at late times for the neutrino equation of state.

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