arXiv subjects

Jorge Enrique García-Farieta

Publications and source records attributed to Jorge Enrique García-Farieta.

15 recordsLinked to original sources

arXiv2025

J-PAS: Forecasting constraints on Neutrino Masses

The large-scale structure survey J-PAS is taking data since October 2023. In this work, we present a forecast based on the Fisher matrix method to establish its sensitivity to the sum of the neutrino masses. We adapt the Fisher Galaxy Survey Code (FARO) to account for the neutrino mass under various configurations applied to galaxy clustering measurements. This approach allows us to test the sensitivity of J-PAS to the neutrino mass across different tracers, with and without non-linear corrections, and under varying sky coverage. We perform our forecast for two cosmological models: $ΛCDM + \sum m_ν$ and $w_0w_a CDM + \sum m_ν$. We combine our J-PAS forecast with Cosmic Microwave Background (CMB) data from the Planck Collaboration and Type Ia supernova (SN) data from Pantheon Plus. Our analysis shows that, for a sky coverage of 8,500 square degrees, J-PAS galaxy clustering data alone will constrain the sum of the neutrino masses to an upper limit at 95% C.L of $\sum m_ν< 0.32$ eV for the $ΛCDM + \sum m_ν$ model, and $\sum m_ν< 0.36$ eV for the $w_0w_a CDM + \sum m_ν$ model. When combined with Planck data, the upper limit improves significantly. For J-PAS+Planck at 95% C.L, we find $\sum m_ν< 0.061$ eV for the $ΛCDM + \sum m_ν$ model, and for J-PAS+Planck+Pantheon Plus, we obtain $\sum m_ν< 0.12$ eV for the $w_0w_a CDM + \sum m_ν$ model. These results demonstrate that J-PAS clustering measurements can play a crucial role in addressing challenges in the neutrino sector, including potential tensions between cosmological and terrestrial measurements of the neutrino mass, as well as in determining the mass ordering.

astro-ph.CO↗

arXiv2025

Bayesian Inference of Primordial Magnetic Field Parameters from CMB with Spherical Graph Neural Networks

Deep learning has emerged as a transformative methodology in modern cosmology, providing powerful tools to extract meaningful physical information from complex astronomical datasets. This paper implements a novel Bayesian graph deep learning framework for estimating key cosmological parameters in a primordial magnetic field (PMF) cosmology directly from simulated Cosmic Microwave Background (CMB) maps. Our methodology utilizes DeepSphere, a spherical convolutional neural network architecture specifically designed to respect the spherical geometry of CMB data through HEALPix pixelization. To advance beyond deterministic point estimates and enable robust uncertainty quantification, we integrate Bayesian Neural Networks (BNNs) into the framework, capturing aleatoric and epistemic uncertainties that reflect the model confidence in its predictions. The proposed approach demonstrates exceptional performance, achieving $R^{2}$ scores exceeding 0.89 for the magnetic parameter estimation. We further obtain well-calibrated uncertainty estimates through post-hoc training techniques including Variance Scaling and GPNormal. This integrated DeepSphere-BNNs framework not only delivers accurate parameter estimation from CMB maps with PMF contributions but also provides reliable uncertainty quantification, providing the necessary tools for robust cosmological inference in the era of precision cosmology.

astro-ph.CO↗

arXiv2025

Parameter sensitivity of cosmic pairwise velocities in the non-linear regime of structure formation

The peculiar velocities of dark matter tracers drive the growth of cosmic structures, providing a sensitive test of cosmological models and strengthening constraints on the nature of dark energy. In this work, we investigate the mean pairwise velocities, $v_{12}$, of dark matter tracers as a cosmological probe in the non-linear regime of cosmic structure formation. Using N-body dark matter-only simulations, we measure $v_{12}$ for pair separations up to 50 $h^{-1}$Mpc and model it by solving the pair conservation equation for a self-gravitating particle system, along with various prescriptions of the nonlinear matter power spectrum. We quantified the sensitivity of $v_{12}$ to variations in key cosmological parameters such as $Ω_{\mathrm{m}}$, $σ_8$, $h$, $M_ν$, and $w$. Our parameter inference analysis using MCMC shows sub-11% agreement with simulation data, with notable degeneracies, particularly between $Ω_\mathrm{m}$ and $σ_8$. We further compute the stable clustering crossing scale across redshifts $z=0$, $0.5$, and $1$, assessing its dependence on cosmology. Among the tested power spectrum modeling approaches, we find that the CSSTEmu emulator provides the most accurate predictions, with deviations below 5% for $r > 10$ $h^{-1}$Mpc at $z=0.5$. Our results are validated using independent simulation suites, demonstrating that our framework offers a robust method for extracting cosmological constraints from upcoming peculiar velocity data.

astro-ph.CO↗

arXiv2025

Individual halo bias in models of $f(R)$ gravity

Halo bias links the statistical properties of the spatial distribution of dark matter halos to those of the underlying dark matter field, providing insights into clustering properties in both general relativity (GR) and modified-gravity scenarios such as $f(R)$ models. While the primary halo mass-dependent bias has been studied in detailed, the secondary bias, which accounts for the additional dependencies on other internal halo properties, can offer a sensitive probe for testing gravity beyond the $Λ$CDM model. To quantify any potential deviations between $Λ$CDM and $f(R)$ gravity models in halo clustering, at both the primary and secondary level, as well as in the distributions of halo properties in the cosmic web. Using $N$-body simulations of $f(R)$ gravity models, we assess the scaling relations and the primary and secondary bias signals of halo populations on the basis of a halo-by-halo estimator of large-scale effective bias. Our analysis is performed using halo number density as the independent variable. The relative difference in the effective bias between the $f(R)$ models and $Λ$CDM is sensitive, albeit slightly, to the power index of modified gravity. The largest deviations from GR are measured for low-mass halos, where the average bias at fixed number density decreases by up to 5\% for fixed scaling indices. We also show that the scaling relations for some environmental properties, including neighbour statistics, Mach number and local overdensity, exhibit small but non-negligible deviations (~3-5\%) from GR for a wide range of number densities. Our results also suggests that the properties of halos in sheets and voids show the largest departures from GR (> 10\% in some cases). In terms of secondary bias, we do not find any statistically significant deviations with respect to $Λ$CDM for any of the properties explored in this work.

astro-ph.CO↗

arXiv2024

Fast simulation mapping: from standard to modified gravity cosmologies using the bias assignment method

We assess the effectiveness of a non-parametric bias model in generating mock halo catalogues for modified gravity (MG) cosmologies, relying on the distribution of dark matter from either MG or $Λ$CDM. We aim to generate halo catalogues that effectively capture the distinct impact of MG, ensuring high accuracy in both two- and three-point statistics for comprehensive analysis of large-scale structures. As part of this study we aim at investigating the inclusion of MG into non-local bias to directly map the tracers onto $Λ$CDM fields, which would save many computational costs. We employ the bias assignment method (BAM) to model halo distribution statistics by leveraging seven high-resolution COLA simulations of MG cosmologies. Taking into account cosmic-web dependencies when learning the bias relations, we design two experiments to map the MG effects: one utilising the consistent MG density fields and the other employing the benchmark $Λ$CDM density field. BAM generates MG halo catalogues from both calibrations experiments excelling in summary statistics, achieving a $\sim 1\%$ accuracy in the power spectrum across a wide range of $k$-modes, with only minimal differences well below 10\% at modes subject to cosmic variance, particularly below $k<0.07$ $h$Mpc$^{-1}$. The reduced bispectrum remains consistent with the reference catalogues within 10\% for the studied configuration. Our results demonstrate that a non-linear and non-local bias description can model the effects of MG starting from a $Λ$CDM dark matter field.

astro-ph.CO↗

arXiv2024

A comprehensive modelling and experimental approach for damped oscillations in U-tubes via Easy JavaScript Simulations

In recent years, science simulations have become popular among educators due to their educational usefulness, availability, and potential for increasing the students' knowledge on scientific topics. In this paper, we introduce the implementation of a user-friendly simulation based on Easy Java/JavaScript Simulations (EJS) to study the problem of damped oscillations in U-tubes. Furthermore, we illustrate various advantages associated with the capabilities of EJS in terms of design and usability in order to encourage teachers to use it as an educational supplement to physics laboratories.

physics.ed-ph↗

arXiv2024

Exploring the magnetic field of Helmholtz and Maxwell coils: a computer-based approach exploiting the superposition principle

Teaching magnetism is one of the most challenging topics at undergraduate level in programmes with scientific background. A basic course includes the description of the magnetic interaction along with empirical results such as the Biot-Savart law's. However, evaluating the magnetic field due to certain current carrying system at any point in space is not an easy task, especially for points in space where symmetry arguments cannot be applied. In this paper we study the magnetic field produced by both Helmholtz and Maxwell coils at all points in space by using a hybrid methodology that combines the superposition principle and an analytical result. We implement a computational approach, that is based on iterating $n$ times the magnetic field produced by a finite current-carrying wire, to evaluate the magnetic field at any point in space for coils arrangements without using advanced calculus. This methodology helps teachers and students to explore the field due to systems with different levels of complexity, combining analytical and computational skills to visualize and analyse the magnetic field. After our analysis, we show that this is an useful approach to emphasize fundamental concepts and mitigate some of the issues that arise when evaluating the magnetic field for systems proposed in introductory physics textbooks.

physics.ed-ph↗

arXiv2024

Simulation of charged particles in Earth's magnetosphere: an approach to the Van Allen belts

Earth's magnetosphere, beyond protecting the ozone layer, is a natural phenomena which allows to study the interaction between charged particles from solar activity and electromagnetic fields. In this paper we studied trajectories of charged particles interacting with a constant dipole magnetic field as first approach of the Earth's magnetosphere using different initial conditions. As a result of this interaction there is a formation of well defined radiation regions by a confinement of charged particles around the lines of the magnetic field. These regions, called Van Allen radiation belts, are described by classical electrodynamics and appear naturally in the numerical modeling done in this work.

physics.space-ph↗

arXiv2024

Bayesian deep learning for cosmic volumes with modified gravity

The new generation of galaxy surveys will provide unprecedented data allowing us to test gravity at cosmological scales. A robust cosmological analysis of the large-scale structure demands exploiting the nonlinear information encoded in the cosmic web. Machine Learning techniques provide such tools, however, do not provide a priori assessment of uncertainties. This study aims at extracting cosmological parameters from modified gravity (MG) simulations through deep neural networks endowed with uncertainty estimations. We implement Bayesian neural networks (BNNs) with an enriched approximate posterior distribution considering two cases: one with a single Bayesian last layer (BLL), and another one with Bayesian layers at all levels (FullB). We train both BNNs with real-space density fields and power-spectra from a suite of 2000 dark matter only particle mesh $N$-body simulations including modified gravity models relying on MG-PICOLA covering 256 $h^{-1}$ Mpc side cubical volumes with 128$^3$ particles. BNNs excel in accurately predicting parameters for $Ω_m$ and $σ_8$ and their respective correlation with the MG parameter. We find out that BNNs yield well-calibrated uncertainty estimates overcoming the over- and under-estimation issues in traditional neural networks. We observe that the presence of MG parameter leads to a significant degeneracy with $σ_8$ being one of the possible explanations of the poor MG predictions. Ignoring MG, we obtain a deviation of the relative errors in $Ω_m$ and $σ_8$ by at least $30\%$. Moreover, we report consistent results from the density field and power spectra analysis, and comparable results between BLL and FullB experiments which permits us to save computing time by a factor of two. This work contributes in setting the path to extract cosmological parameters from complete small cosmic volumes towards the highly nonlinear regime.

astro-ph.CO↗

arXiv2022

Universality of the halo mass function in modified gravity cosmologies

We study the halo mass function (HMF) in modified gravity (MG) models using a set of large $N$-body simulations -- the ELEPHANT suite. We consider two popular beyond-general relativity scenarios: the Hu-Sawicki chameleon $f(R)$ model and the normal branch of the Dvali-Gabadadze-Porrati braneworld (nDGP). We show that in MG, analytic formulation based on the Press-Schechter framework offers a grossly inaccurate description of the HMF. We find, however, that once the HMF is expressed in terms of the dimensionless multiplicity function, it approximately assumes a redshift-independent universal character for all the models. Exploiting this property, we propose universal fits for the MG HMF in terms of their fractional departures from the $\mathrm{Λ\text{CDM}}$ case. We find two enclosed formulas, one for $f(R)$ and another for nDGP, that provide a reliable description of the HMF over the mass range covered by the simulations. These are accurate to a few percent with respect to the $N$-body data. We test the extrapolation potential of our fits against separate simulations with a different cosmological background and mass resolution, and find very good accuracy, within $\sim 10\%$. A particularly interesting finding from our analysis is a Gaussian-like shape of the HMF deviation that seems to appear universally across the whole $f(R)$ family, peaking at a mass variance scale characteristic for each $f(R)$ variant. We attribute this behavior to the specific physics of the environmentally-dependent chameleon screening models.

astro-ph.CO↗

arXiv2021

C$^3$-Cluster Clustering Cosmology I. New constraints on the cosmic growth rate at z~0.3 from redshift-space clustering anisotropies

Redshift-space distortions in the clustering of galaxy clusters provide a novel probe to test the gravity theory on cosmological scales. The aim of this work is to derive new constraints on the linear growth rate of cosmic structures from the redshift-space two-point correlation function of galaxy clusters. We construct a large spectroscopic catalogue of optically-selected clusters from the Sloan Digital Sky Survey. The selected sample consists of $43,743$ clusters in the redshift range $0.1<z<0.42$, with masses estimated from weak-lensing calibrated scaling relations. We measure the transverse and radial wedges of the two-point correlation function of the selected clusters. Modelling the redshift-space clustering anisotropies, we provide the first constraints on the linear growth rate from cluster clustering. The cluster masses are used to set a prior on the linear bias of the sample. This represents the main advantage in using galaxy clusters as cosmic probes, instead of galaxies. Assuming a standard cosmological model consistent with the latest cosmic microwave background constraints, we do not find any evidence of deviations from General Relativity. Specifically, we get the value of the growth rate times the matter power spectrum normalisation parameter $fσ_{8}=0.44\pm0.05$, at an effective redshift $z=0.275$.

astro-ph.CO↗

arXiv2021

Probing gravity with redshift-space distortions: effects of tracer bias and sample selection

We investigate clustering properties of dark matter halos and galaxies to search for optimal statistics and scales where possible departures from general relativity (GR) could be found. We use large N-body cosmological simulations to perform measurements based on the two-point correlation function (2PCF) in GR and in selected modified gravity (MG) structure formation scenarios. As a test-bed, we employ two popular beyond-GR models: $f(R)$ gravity and the normal branch of the Dvali-Gabadadze-Porrati (nDGP) braneworld. We study a range of simulated halo and galaxy populations and reveal a noticeable MG signal in the monopole and quadrupole moments of the redshift-space 2PCF, and in the so-called clustering wedges. However, once expressed in terms of the linear distortion parameter, $β$, the statistical significance of these signals largely diminishes due to a strong degeneracy between MG-enhanced clustering and modified tracer bias. To circumvent this, we consider statistics less dependent on the bias: relative clustering ratios. We generalize the monopole ratio proposed in earlier work to multipole moments and clustering wedges, and introduce a new estimator of the $β$ parameter. The clustering ratios we extract foster noticeable differences between MG and GR models, reaching a maximum deviation of 10\% at 2$σ$ significance for specific variants of $f(R)$ and nDGP. We show that such departures could be measured for $β$ if non-linear effects at intermediate scales are correctly modeled. Our study indicates that the clustering ratios give great promise to search for signatures of MG in the large-scale structure. We also find that the selection of an optimal tracer sample depends on a particular statistics and gravity model to be considered.

astro-ph.CO↗

arXiv2020

Validating the methodology for constraining the linear growth rate from clustering anisotropies

Redshift-space clustering distortions provide one of the most powerful probes to test the gravity theory on the largest cosmological scales. We perform a systematic validation study of the state-of-the-art statistical methods currently used to constrain the linear growth rate from redshift-space distortions in the galaxy two-point correlation function. The numerical pipelines are tested on mock halo catalogues extracted from large N-body simulations of the standard cosmological framework. We consider both the monopole and quadrupole multipole moments of the redshift-space two-point correlation function, as well as the radial and transverse clustering wedges, in the comoving scale range $10 30$ \Mpch, the discrepancy is reduced below $5\%$. At higher redshifts, we find instead an overall good agreement between measurements and model predictions. Though this accuracy is good enough for clustering analyses in current redshift surveys, the models have to be further improved not to introduce significant systematics in RSD constraints from next generation galaxy surveys. The effect of redshift errors is degenerate with the one of small-scale random motions, and can be marginalised over in the statistical analysis, not introducing any statistically significant bias in the linear growth constraints, especially at $z\geq1$.

astro-ph.CO↗

arXiv2020

Beyond the magnetic field of a finite wire: a teaching approach using the superposition principle

In the introductory courses on electromagnetism, the Biot-Savart law is generally explained by a simple example to find the magnetic field created at any point in space by a small wire element that carries a current. The simplest system studied consists in a straight finite wire, however, to explore the magnetic field in complex geometries is required more imagination to solve the mathematics involved. In this paper, we present a practical methodology to use the superposition principle of magnetic fields by using n-times a finite current-carrying wire to evaluate the magnetic field at any point in space for various geometric configurations. This approach allows students to explore systems with different levels of complexity, combining analytical and computational skills to visualize and analyze the magnetic field.

physics.ed-ph↗

arXiv2019

Clustering and redshift-space distortions in modified gravity models with massive neutrinos

Modified gravity and massive neutrino cosmologies are two of the most interesting scenarios that have been recently explored to account for possible observational deviations from the concordance $Λ$-cold dark matter ($Λ$CDM) model. In this context, we investigated the large-scale structure of the Universe by exploiting the \dustp simulations that implement, simultaneously, the effects of $f(R)$ gravity and massive neutrinos. To study the possibility of breaking the degeneracy between these two effects, we analysed the redshift-space distortions in the clustering of dark matter haloes at different redshifts. Specifically, we focused on the monopole and quadrupole of the two-point correlation function, both in real and redshift space. The deviations with respect to $Λ$CDM model have been quantified in terms of the linear growth rate parameter. We found that redshift-space distortions provide a powerful probe to discriminate between $Λ$CDM and modified gravity models, especially at high redshifts ($z \gtrsim 1$), even in the presence of massive neutrinos.

astro-ph.CO↗