arXiv subjects

J. Alberto Vázquez

Publications and source records attributed to J. Alberto Vázquez.

At least 19 recordsLinked to original sources

arXiv2026

Axio-Dilaton Dark Energy: A Dynamical Systems and Bayesian Inference Analysis

We initiate a systematic cosmological investigation of an axio-dilaton system governed by a curved field-space metric as generically motivated by supergravity and string compactifications. The scalar sector consists of a light scalar dilaton field evolving along an asymptotically exponential potential and a pseudoscalar field spanning an essentially flat direction, kinetically coupled through an exponential field-space metric. Deviations from the asymptotic regime are incorporated by introducing a generalised Albrecht-Skordis potential containing a polynomial factor. Utilising a dynamical systems approach alongside numerical evolution and Bayesian inference, we constrain the model using Type Ia Supernovae (SNe Ia), Baryon Acoustic Oscillations (BAO) and Planck 2018 distance-prior data. We identify a parameter degeneracy indicating that steeper potentials demand stronger field-space kinetic couplings to sustain late-time cosmic acceleration. Finally, a joint likelihood analysis reveals that while the model successfully accounts for dark energy dynamics, it is only slightly statistically favoured over $Λ$CDM by current observations.

astro-ph.CO↗

arXiv2026

Reconstructing sign-switching dark energy histories: Scalar-field regularity, conditional potential comparison, and representative dynamics

Phenomenologically similar sign-switching dark-energy histories need not have comparable scalar-field realizations. We reconstruct minimally coupled scalars with fixed kinetic sign for three prescribed histories: the error-function model (ECDM), the compact smooth-step model (SSCDM), and the ladder-like model (L$Λ$CDM). Continuous negative-to-positive density crossings select the phantom branch. ECDM yields a smooth on-shell potential at every finite redshift; its equation-of-state pole at the density zero is only a ratio singularity. Exact SSCDM has a regular trajectory but a $C^1$, non-$C^2$ endpoint potential, $V-V_e\propto|ϕ-ϕ_e|^{4/3}$. Its non-Lipschitz force permits delayed departures from frozen plateaus, so the reconstructed potential and plateau data do not uniquely generate the prescribed history. The exact Ladder requires distributional kinetic stress and has no ordinary classical realization in the adopted one-field action. In conditional synthetic comparisons, the sigmoid--Gaussian family ranks highest for ECDM and the generalized axion-like family for SSCDM, although the fitted axion exponents $n<1/2$ imply a divergent endpoint force. Representative regular forward solutions exhibit negative-to-positive scalar-density crossings, with the potential zero preceding the density zero. In a closure test, unretuned evolution of the top-ranked ECDM template tracks the target approximately, $\max|Δ\widetildeΩ_ϕ|\simeq0.16$. Closure fails structurally for compact SSCDM: no potential with a locally Lipschitz force can reproduce its exact finite-duration frozen plateaus from exactly frozen initial data. Field-map existence, invertibility, and endpoint regularity must be assessed before interpreting a phenomenological history as scalar dynamics. The phantom action is used only as a homogeneous effective proxy.

gr-qc↗

arXiv2026

Cosmological Constraints on Interacting Scalar-Field Dark Matter and Early Dark Energy mediated by a Kinetic Kernel

In this work, we build a general framework for constraining interacting scalar fields with cosmological observations, applicable to a broad family of scalar--field potentials and interaction kernels. As a representative example, we derive constraints on a model in which dark matter is described by a scalar field $ϕ$ with a quadratic potential and dark energy by a scalar field $ψ$ with the Albrecht--Skordis potential, considering both the uncoupled case and a more general scenario in which the exchange of energy occurs through an interaction kernel proportional to the kinetic energies of the scalar fields $Q \propto \dotϕ^2 \dotψ^2$. The scalar field parameters are constrained by using \textit{Planck} 2018 CMB data, DESI BAO measurements, Ly-$α$ forest constraints, and SNeIa observations, and we find that the scalar field parameters are driven toward their $Λ$CDM-like limits; the early dark energy fraction is largely suppressed, and the SFDM mass is bounded to values for which the field behaves close to cold dark matter. In the interacting case, the results show no significant preference for the transfer of energy between dark energy and dark matter at 95\% C.L. In general, the model keeps $H_0$ close to the Planck-$Λ$CDM range and provides best-fit $χ^2$ values comparable to those of the corresponding $Λ$CDM fits.

physics.gen-ph↗

arXiv2026

Variational autoencoder for generating realistic $N$-body simulations for dark matter halos

In this paper, we present a deep-learning approach to generate synthetic cosmological images by training a convolutional variational autoencoder on two-dimensional dark matter density slices projected from $Λ$CDM $N$-body simulations. The model learns a compact latent representation that enables accurate reconstructions and fast generation of new synthetic realizations through a single forward pass through the decoder. We validate the generated fields using cosmology-based summary statistics, focusing on the matter power spectrum and related Fourier space diagnostics, and found good agreement with the reference simulation across the range of scales where the maps exhibit good resolution. Thanks to its low inference cost and stable training target, this variational-autoencoder approach provides a lightweight and reproducible basis for generative modeling of large-scale projected structures and can support downstream tasks such as fast simulation generation and data augmentation.

astro-ph.CO↗

arXiv2026

Data-driven modeling of rotation curves with artificial neural networks

Galactic rotation curves are crucial for understanding the distribution of mass in galaxies. Despite advances in precision observations, there are discrepancies between the inferred mass from luminosity and the observed rotational velocities, often attributed to dark matter. While traditional parametric models provide valuable insights, they struggle with complex galactic features like prominent bulges and non-circular motions. In this study, we apply artificial neural networks to generate robust, data-driven models, tailored to each galaxy, for the rotation curves of spiral galaxies using high-quality observational data. Our approach demonstrates that neural networks can effectively capture the intricate structure of rotation curves without relying on predefined astrophysical assumptions. By comparing the data-based models with the Navarro-Frenk-White model under two different assumptions for the stellar component, we classify galaxies based on the model that best fits their rotation curves, offering insights into the limitations and strengths of both theoretical and data-based methods. This work highlights the potential of machine learning techniques in identifying galaxies whose dynamics are not well captured by standard theoretical models, pointing to the need for more refined physical descriptions.

astro-ph.GA↗

arXiv2026

Late-time acceleration without a vacuum term in ${f(R,L_m)}$ gravity: scaling deSitter dynamics and parameter constraints

We investigate late-time cosmic acceleration in $f(R,L_m)$ gravity driven by nonlinear matter contributions, focusing on the class $f(R,L_m)=R/2+c_1 L_m+c_n L_m^{n}+c_0$ with the explicit choice $L_m=ρ_m$ and an uncoupled radiation sector. We analyze two realizations: (i) Case A: $f(R,L_m)=R/2+βρ_m^{n}+γ$, where $γ$ acts as a vacuum term, and (ii) Case B: $f(R,L_m)=R/2+βρ_m+γρ_m^{n}$, where the nonlinear sector can mimic dark energy without an explicit cosmological constant. For each case, we construct a bounded autonomous system, classify all critical points and their stability, and compute cosmographic diagnostics. The phase-space analysis shows that Case A reproduces the standard radiation$\to$matter$\to$de~Sitter sequence only for $n\gtrsim 4/5$, with acceleration essentially enforced by the vacuum term. In contrast, Case~B admits a qualitatively distinct and phenomenologically appealing branch: for $0<n<1/2$ the system possesses a physical \emph{scaling} de~Sitter future attractor inside the bounded simplex, yielding radiation$\to$matter$\to$acceleration with $q=-1$ and $ω_{\rm eff}=-1$ and without introducing $c_0$. We confront both models with background data (CC, Union3, DESI BAO, plus a BBN prior on $Ω_b h^2$) using nested sampling and perform model comparison via Bayesian evidence and AIC/BIC. The full data combination constrains $n=1.08\pm0.05$ in Case A and $n=0.05\pm0.10$ in Case B (68\% CL), the latter lying within the accelerating window while remaining statistically consistent with $Λ$CDM kinematics at the background level. We also record minimal consistency conditions for stability (tensor no-ghost and luminal propagation) and motivate a dedicated perturbation-level analysis as the next step to test growth and lensing observables.

astro-ph.CO↗

arXiv2025

Testing an oscillatory behavior of dark energy

The main aim of this work is to use a model-independent approach, along with late-time observational probes, to reconstruct the dark energy (DE) equation of state $w_{\rm DE}(z)$. Our analysis showed that, for a late time universe, $w_{\rm DE}$ deviates from being a constant but in contrast exhibits an oscillatory behavior, hence both quintessence ($w_{\rm DE}> -1$) and phantom ($w_{\rm DE} < -1$) regimes are equally allowed. In order to portray this oscillatory behavior, we explored various parametrizations for the equation of state and identified the closest approximation based on the goodness of fit with the data and the Bayesian evidence analysis. Our findings indicated that while all considered oscillating DE parametrizations provided a better fit to the data, compared to the cosmological constant, they are penalized in the Bayesian evidence analysis due to the additional free parameters. Overall, the present article demonstrates that in the low redshift regime, the equation of state of the DE prefers to be dynamical and oscillating. We anticipate that future cosmological probes will take a stand in this direction.

astro-ph.CO↗

arXiv2024

Non-parametric reconstruction of cosmological observables using Gaussian Processes Regression

The current accelerated expansion of the Universe remains ones of the most intriguing topics in modern cosmology, driving the search for innovative statistical techniques. Recent advancements in machine learning have significantly enhanced its application across various scientific fields, including physics, and particularly cosmology, where data analysis plays a crucial role in problem-solving. In this work, a non-parametric regression method with Gaussian processes is presented along with several applications to reconstruct some cosmological observables, such as the deceleration parameter and the dark energy equation of state, in order to contribute with some information that helps to clarify the behavior of the Universe. It was found that the results are consistent with $Λ$CDM and the predicted value of the Hubble parameter at redshift zero is $H_{0}=68.798\pm 6.340(1σ) \text{ km}\text{ s}^{-1}\text{ Mpc}^{-1}$.

astro-ph.CO↗

arXiv2024

Deep Learning and genetic algorithms for cosmological Bayesian inference speed-up

In this paper, we present a novel approach to accelerate the Bayesian inference process, focusing specifically on the nested sampling algorithms. Bayesian inference plays a crucial role in cosmological parameter estimation, providing a robust framework for extracting theoretical insights from observational data. However, its computational demands can be substantial, primarily due to the need for numerous likelihood function evaluations. Our proposed method utilizes the power of deep learning, employing feedforward neural networks to approximate the likelihood function dynamically during the Bayesian inference process. Unlike traditional approaches, our method trains neural networks on-the-fly using the current set of live points as training data, without the need for pre-training. This flexibility enables adaptation to various theoretical models and datasets. We perform simple hyperparameter optimization using genetic algorithms to suggest initial neural network architectures for learning each likelihood function. Once sufficient accuracy is achieved, the neural network replaces the original likelihood function. The implementation integrates with nested sampling algorithms and has been thoroughly evaluated using both simple cosmological dark energy models and diverse observational datasets. Additionally, we explore the potential of genetic algorithms for generating initial live points within nested sampling inference, opening up new avenues for enhancing the efficiency and effectiveness of Bayesian inference methods.

astro-ph.IM↗

arXiv2024

Interacting Scalar fields: Dark matter and early dark energy

The main aim of this work is to explore the possibility that cold dark matter (CDM) and early dark energy (EDE) can be described by canonical scalar fields that are coupled at the level of its conservation equations. The formalism covers dynamical aspects at the background and linear perturbation levels for an arbitrary coupling function, followed by an example of it. We emphasize the impact of this model on the Matter Power Spectrum and the Cosmic Microwave Background (CMB) spectra, with or without direct interaction. Our findings indicate that the presence of a scalar field can partially counteract the known effects of the other, opening the possibility to avoid some undesired aspects, such as the increase in $Ω_{m}$ that usually is needed in the case of a purely EDE scalar field scenario, in order to fit the CMB spectra. This opens up the possibility to analyzing whether the interaction can help to ameliorate the cosmological tensions.

astro-ph.CO↗

arXiv2024

Coupled Multi Scalar Field Dark Energy

The main aim of this paper is to present the multi scalar field components as candidates to be the dark energy of the universe and their observational constraints. We start with the canonical Quintessence and Phantom fields with quadratic potentials and show that a more complex model should bear in mind to satisfy current cosmological observations. Then we present some implications for a combination of two fields, named as Quintom models. We consider two types of models, one as the sum of the quintessence and phantom potentials and other including an interacting term between fields. We find that adding one extra degree of freedom, by the interacting term, the dynamics enriches considerably and could lead to an improvement in the fit of $-2\lnΔ\Like_{\rm max}= 5.19$, compared to $Λ$CDM. The resultant effective equation of state is now able to cross the phantom divide line, and in several cases present an oscillatory or discontinuous behavior, depending on the interaction value. The parameter constraints of the scalar field models (quintessence, phantom, quintom and interacting quintom) were performed using Cosmic Chronometers, Supernovae Ia and Baryon Acoustic Oscillations data; and the Log-Bayes factors were computed to compare the performance of the models. We show that single scalar fields may face serious troubles and hence the necessity of a more complex models, i.e. multiple fields.

astro-ph.CO↗

arXiv2024

Cosmological parameter estimation with Genetic Algorithms

Genetic algorithms are a powerful tool in optimization for single and multi-modal functions. This paper provides an overview of their fundamentals with some analytical examples. In addition, we explore how they can be used as a parameter estimation tool in cosmological models to maximize the likelihood function, complementing the analysis with the traditional Markov Chain Monte Carlo methods. We analyze that genetic algorithms provide fast estimates by focusing on maximizing the likelihood function, although they cannot provide confidence regions with the same statistical meaning as Bayesian approaches. Moreover, we show that implementing sharing and niching techniques ensures an effective exploration of the parameter space, even in the presence of local optima, always helping to find the global optima. This approach is invaluable in the cosmological context, where exhaustive space exploration of parameters is essential. We use dark energy models to exemplify the use of genetic algorithms in cosmological parameter estimation, including a multimodal problem, and we also show how to use the output of a genetic algorithm to obtain derived cosmological functions. This paper concludes that genetic algorithms are a handy tool within cosmological data analysis, without replacing the traditional Bayesian methods but providing different advantages.

astro-ph.CO↗

arXiv2023

Cosmology on a Gravitational Wave Background

It is a fact that the universe lives on a Gravitational Wave Background (GWB), which it may be in the form of extra energy, which is not contained in Einstein's field equations. In \cite{Matos:2021jef}, a new model was developed to explain the current accelerating expansion of the universe where a GWB was incorporated by extending Einstein's equations to $R_{μν}-\frac{1}{2}Rg_{μν}+\frac{2π^2}{λ^2}g_{μν}=κ^2 T_{μν}$, where $λ$ is the Compton wavelength of the graviton. In the present work we show that this extended form agrees very well with the observations of Cosmic chronometers, Baryon Acoustic Oscillations and Pantheon SN type Ia, reproducing the observational data with a $Δχ^2=3.26$ in favor of the present model compared to the $Λ$CDM. The values favored by these observations are $Ω_{\rm m} = 0.31 \pm 0.02$, $H_0=68 \pm 0.02$ Km/s/Mpc, $Ω_{\rm k} = 0.001\pm 0.011$; we also find an excellent consistence of this model with the Cosmic Microwave Background and the Matter Power Spectrum. We conclude that this model is an excellent alternative to explain the accelerating expansion of the universe without incorporating the cosmological constant.

physics.gen-ph↗

arXiv2023

Neural network reconstructions for the Hubble parameter, growth rate and distance modulus

This paper introduces a new approach to reconstruct cosmological functions using artificial neural networks based on observational measurements with minimal theoretical and statistical assumptions. By using neural networks, we can generate computational models of observational datasets, and then we compare them with the original ones to verify the consistency of our method. This methodology is applicable to even small-size datasets. In particular, we test the proposed method with data coming from cosmic chronometers, $fσ_8$ measurements, and the distance modulus of the Type Ia supernovae. Furthermore, we introduce a first approach to generate synthetic covariance matrices through a variational autoencoder, using the systematic covariance matrix of the Type Ia supernova compilation.

astro-ph.CO↗

arXiv2023

Neural Networks Optimized by Genetic Algorithms in Cosmology

The applications of artificial neural networks in the cosmological field have shone successfully during the past decade, this is due to their great ability of modeling large amounts of datasets and complex nonlinear functions. However, in some cases, their use still remains controversial because their ease of producing inaccurate results when the hyperparameters are not carefully selected. In this paper, to find the optimal combination of hyperparameters to artificial neural networks, we propose to take advantage of the genetic algorithms. As a proof of the concept, we analyze three different cosmological cases to test the performance of the architectures achieved with the genetic algorithms and compare them with the standard process, consisting of a grid with all possible configurations. First, we carry out a model-independent reconstruction of the distance modulus using a type Ia supernovae compilation. Second, the neural networks learn to infer the equation of state for the quintessence model, and finally with the data from a combined redshift catalog the neural networks predict the photometric redshift given six photometric bands (urgizy). We found that the genetic algorithms improve considerably the generation of the neural network architectures, which can ensure more confidence in their physical results because of the better performance in the metrics with respect to the grid method.

astro-ph.IM↗

arXiv2022

Cosmological constraints on the Multi Scalar Field Dark Matter model

The main aim of this paper is to provide cosmological constraints on the Multi Scalar Field Dark Matter model (MSFDM), in which we assume the dark matter is made up of different ultra-light scalar fields. As a first approximation, we consider they are real and do not interact with each other. We study the equations for both the background and perturbations for $N$-fields and present the evolution of the density parameters, the mass power spectrum and the CMB spectrum. In particular, we focus on two scalar fields with several combinations for the potentials $V(ϕ) = 1/2 m_ϕ^2 ϕ^2$, $V(ϕ) = m_ϕ^2f^2\left[1+\cos(ϕ/f)\right]$ and $V(ϕ) = m_ϕ^2f^2\left[\cosh(ϕ/f)-1\right]$, however the work, along with the code, could be easily extended to more fields. We use the data from BAO, Big Bang Nucleosynthesis, Lyman-$α$ forest and Supernovae to find constraints on the sampling parameters for the cases of a single field and double field, along with the Bayesian evidence. We found that some combinations of the potentials get penalized through the evidence, however for others there is a preference as good as for the cold dark matter.

astro-ph.CO↗

arXiv2022

Observational cosmology with Artificial Neural Networks

In cosmology, the analysis of observational evidence is very important to test theoretical models of the Universe. Artificial neural networks are powerful and versatile computational tools for data modelling and are recently being considered in the analysis of cosmological data. The main goal of this paper is to provide an introduction to artificial neural networks and to describe some applications to cosmology. We present an overview on the fundamentals of neural networks and their technical details. Throughout three examples, we show their capabilities in modelling cosmological data, saving computational time in numerical tasks, and classifying stellar objects. Artificial neural networks offer interesting qualities that make them a viable alternative method for data analysis in cosmological research.

astro-ph.CO↗

arXiv2021

Classification algorithms applied to structure formation simulations

Throughout cosmological simulations, the properties of the matter density field in the initial conditions have a decisive impact on the features of the structures formed today. In this paper we use a random-forest classification algorithm to infer whether or not dark matter particles, traced back to the initial conditions, would end up in dark matter halos whose masses are above some threshold. This problem might be posed as a binary classification task, where the initial conditions of the matter density field are mapped into classification labels provided by a halo finder program. Our results show that random forests are effective tools to predict the output of cosmological simulations without running the full process. These techniques might be used in the future to decrease the computational time and to explore more efficiently the effect of different dark matter/dark energy candidates on the formation of cosmological structures.

astro-ph.CO↗