arXiv subjects

Jurgen Mifsud

Publications and source records attributed to Jurgen Mifsud.

At least 19 recordsLinked to original sources

arXiv2026

Model-independent calibration of Gamma-Ray Bursts with neural networks

The $Λ$ Cold Dark Matter ($Λ$CDM) cosmological model has been highly successful in predicting cosmic structure and evolution, yet recent precision measurements have highlighted discrepancies, especially in the Hubble constant inferred from local and early-Universe data. Gamma-ray bursts (GRBs) present a promising alternative for cosmological measurements, capable of reaching higher redshifts than traditional distance indicators. This work leverages GRBs to refine cosmological parameters independently of the $Λ$CDM framework. Using the Platinum compilation of long GRBs, we calibrate the Dainotti relations-empirical correlations among GRB luminosity properties-as standard candles through artificial neural networks (ANNs). We analyze both the 2D and 3D Dainotti calibration relations, leveraging an ANN-driven Markov Chain Monte Carlo approach to minimize scatter in the calibration parameters, thereby achieving a stable Hubble diagram. This ANN-based calibration approach offers advantages over Gaussian processes, avoiding issues such as kernel function dependence and overfitting. Our results emphasize the need for model-independent calibration approaches to address systematic challenges in GRB luminosity variability, ultimately extending the cosmic distance ladder in a robust way. By addressing redshift evolution and reducing systematic uncertainties, GRBs can serve as reliable high-redshift distance indicators, offering critical insights into current cosmological tensions.

astro-ph.CO↗

arXiv2025

The CosmoVerse White Paper: Addressing observational tensions in cosmology with systematics and fundamental physics

The standard model of cosmology has provided a good phenomenological description of a wide range of observations both at astrophysical and cosmological scales for several decades. This concordance model is constructed by a universal cosmological constant and supported by a matter sector described by the standard model of particle physics and a cold dark matter contribution, as well as very early-time inflationary physics, and underpinned by gravitation through general relativity. There have always been open questions about the soundness of the foundations of the standard model. However, recent years have shown that there may also be questions from the observational sector with the emergence of differences between certain cosmological probes. In this White Paper, we identify the key objectives that need to be addressed over the coming decade together with the core science projects that aim to meet these challenges. These discordances primarily rest on the divergence in the measurement of core cosmological parameters with varying levels of statistical confidence. These possible statistical tensions may be partially accounted for by systematics in various measurements or cosmological probes but there is also a growing indication of potential new physics beyond the standard model. After reviewing the principal probes used in the measurement of cosmological parameters, as well as potential systematics, we discuss the most promising array of potential new physics that may be observable in upcoming surveys. We also discuss the growing set of novel data analysis approaches that go beyond traditional methods to test physical models. [Abridged]

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arXiv2024

A possible late-time transition of $M_B$ inferred via neural networks

The strengthening of tensions in the cosmological parameters has led to a reconsideration of fundamental aspects of standard cosmology. The tension in the Hubble constant can also be viewed as a tension between local and early Universe constraints on the absolute magnitude $M_B$ of Type Ia supernova. In this work, we reconsider the possibility of a variation of this parameter in a model-independent way. We employ neural networks to agnostically constrain the value of the absolute magnitude as well as assess the impact and statistical significance of a variation in $M_B$ with redshift from the Pantheon+ compilation, together with a thorough analysis of the neural network architecture. We find an indication for a possible transition redshift at the $z\approx 1$ region.

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arXiv2024

Non-fluid like Boltzmann code architecture for early times f(T) cosmologies

There have been several works that have studied scalar cosmological perturbations in $f(T)$ teleparallel gravity theories to understand early cosmic times dynamics. In this direction, the perturbations presented have been performed by considering $f(T)$ extensions as an effective fluid-like scheme, where the equation-of-state contains extra terms due to the torsion. In this work, we discuss introducing a non-fluid-like approach as a direct consequence of $f(T)$ extensions, particularly for $f(T)$ power law model scenarios. This approach will be compared using CMB constraints data from Planck 2018 and SDSS catalogs, showing a change in about 17% in $C_{l}$ at $l< 10^{1}$ from the ones reported in the literature as a fluid-like approach, which will bring significant changes in the analysis on cosmological tensions at early cosmic times.

gr-qc↗

arXiv2024

Growth of structures using redshift space distortion in $f(T)$ Cosmology

Cosmology faces a pressing challenge with the Hubble constant ($H_0$) tension, where the locally measured rate of the Universe's expansion does not align with predictions from the cosmic microwave background (CMB) calibrated with $Λ$CDM model. Simultaneously, there is a growing tension involving the weighted amplitude of matter fluctuations, known as $S_{8,0}$ tension. Resolving both tensions within one framework would boost confidence in any one particular model. In this work, we analyse constraints in $f(T)$ gravity, a framework that shows promise in shedding light on cosmic evolution. We thoroughly examine prominent $f(T)$ gravity models using a combination of data sources, including Pantheon+ (SN), cosmic chronometers (CC), baryonic acoustic oscillations (BAO) and redshift space distortion (RSD) data. We use these models to derive a spectrum of $H_0$ and $S_{8,0}$ values, aiming to gauge their ability to provide insights into, and potentially address, the challenges posed by the $H_0$ and $S_{8,0}$ tensions.

astro-ph.CO↗

arXiv2023

Neural network reconstruction of cosmology using the Pantheon compilation

In this work, we reconstruct the Hubble diagram using various data sets, including correlated ones, in Artificial Neural Networks (ANN). Using ReFANN, that was built for data sets with independent uncertainties, we expand it to include non-Guassian data points, as well as data sets with covariance matrices among others. Furthermore, we compare our results with the existing ones derived from Gaussian processes and we also perform null tests in order to test the validity of the concordance model of cosmology.

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arXiv2023

Neural network reconstruction of scalar-tensor cosmology

Neural networks have shown great promise in providing a data-first approach to exploring new physics. In this work, we use the full implementation of late time cosmological data to reconstruct a number of scalar-tensor cosmological models within the context of neural network systems. In this pipeline, we incorporate covariances in the data in the neural network training algorithm, rather than a likelihood which is the approach taken in Markov chain Monte Carlo analyses. For general subclasses of classic scalar-tensor models, we find stricter bounds on functional models which may help in the understanding of which models are observationally viable.

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arXiv2023

Constraints on $f(T)$ Cosmology with Pantheon+

$f(T)$ cosmology has shown promise in explaining aspects of cosmic evolution. In this work, we analyze constraints on leading models of $f(T)$ gravity in the context of the recently released Pantheon+ data set, together with comparisons with previous releases. We also consider other late-time data sets including cosmic chronometers and baryonic acoustic oscillation data. Our main result is that we find that the different $f(T)$ models under investigation connect to a variety of Hubble constant, which may help alleviate the cosmic tension on this parameter.

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arXiv2023

Teleparallel Gravity: From Theory to Cosmology

Teleparallel gravity has significantly increased in popularity in recent decades, bringing attention to Einstein's other theory of gravity. In this Review, we relate this form of geometry to the broader metric-affine approach to forming gravitational theories where we describe a systematic way of constructing consistent teleparallel theories that respect certain physical conditions such as local Lorentz invariance. We first use teleparallel gravity to formulate a teleparallel equivalent of general relativity which is dynamically equivalent to general relativity but which may have different behaviors for other scenarios, such as quantum gravity. After setting this foundation, we describe the plethora of modified teleparallel theories of gravity that have been proposed in the literature. In the second part of the Review, we first survey works in teleparallel astrophysics literature where we focus on the open questions in this regime of physics. We then discuss the cosmological consequences for the various formulations of teleparallel gravity. We do this at background level by exploring works using various approaches ranging from dynamical systems to Noether symmetries, and more. Naturally, we then discuss perturbation theory, firstly by giving a concise approach in which this can be applied in teleparallel gravity theories and then apply it to a number of important theories in the literature. Finally, we examine works in observational and precision cosmology across the plethora of proposal theories. This is done using some of the latest observations and is used to tackle cosmological tensions which may be alleviated in teleparallel cosmology. We also introduce a number of recent works in the application of machine learning to gravity, we do this through deep learning and Gaussian processes, together with discussions about other approaches in the literature.

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arXiv2022

$f(T,B)$ Gravity in the late Universe in the context of local measurements

We explore the viability of three models in $f(T,B)$ gravity using data from recent surveys based on cosmic chronometers, the Pantheon data set, and baryonic acoustic oscillation data. We also assess the consistency of these models and data set combinations with two important priors on the Hubble constant coming from the SH0ES Team and measurements using the tip of the red giant branch respectively. These give the highest and lowest values of the Hubble constant coming from cosmology independent studies. In general, our analysis does provide a more consistent fit for the late time data being analyzed. However, each model does include an additional model parameter in comparison with the concordance model. We close the analysis with a comparative analysis in which each model, data set and Hubble constant prior combination are cross-analyzed against each other.

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arXiv2022

On the Robustness of the Constancy of the Supernova Absolute Magnitude: Non-parametric Reconstruction \& Bayesian approaches

In this work, we test the robustness of the constancy of the Supernova absolute magnitude $M_B$ using Non-parametric Reconstruction Techniques (NRT). We isolate the luminosity distance parameter $d_L(z)$ from the Baryon Acoustic Oscillations (BAO) data set and cancel the expansion part from the observed distance modulus $μ(z)$. Consequently, the degeneracy between the absolute magnitude and the Hubble constant $H_0$, is replaced by a degeneracy between $M_B$ and the sound horizon at drag epoch $r_d$. When imposing the $r_d$ value, this yields the $M_B(z) = M_B + δM_B(z)$ value from NRT. We perform the respective reconstructions using the model independent Artificial Neural Network (ANN) technique and Gaussian processes (GP) regression. For the ANN we infer $M_B = -19.22\pm0.20$, and for the GP we get $M_B = -19.25\pm0.39$ as a mean for the full distribution when using the sound horizon from late time measurements. These estimations provide a $1\,σ$ possibility of a nuisance parameter presence $δM_B(z)$ at higher redshifts. We also tested different known nuisance models with the Markov Chain Monte Carlo (MCMC) technique which showed a strong preference for the constant model, but it was not possible not single out a best fit nuisance model.

astro-ph.CO↗

arXiv2022

Neural Network Reconstruction of $H'(z)$ and its application in Teleparallel Gravity

In this work, we explore the possibility of using artificial neural networks to impose constraints on teleparallel gravity and its $f(T)$ extensions. We use the available Hubble parameter observations from cosmic chronometers and baryon acoustic oscillations from different galaxy surveys. We discuss the procedure for training a network model to reconstruct the Hubble diagram. Further, we describe the procedure to obtain $H'(z)$, the first order derivative of $H(z)$, using artificial neural networks which is a novel approach to this method of reconstruction. These analyses are complemented with further studies on the impact of two priors which we put on $H_0$ to assess their impact on the analysis, which are the local measurements by the SH0ES team ($H_0^{\text{R20}} = 73.2 \pm 1.3$ km Mpc$^{-1}$ s$^{-1}$) and the updated TRGB calibration from the Carnegie Supernova Project ($H_0^{\text{TRGB}} = 69.8 \pm 1.9$ km Mpc$^{-1}$ s$^{-1}$), respectively. Additionally, we investigate the validity of the concordance model, through some cosmological null tests with these reconstructed data sets. Finally, we reconstruct the allowed $f(T)$ functions for different combinations of the observational Hubble data sets. Results show that the $Λ$CDM model lies comfortably included at the 1$σ$ confidence level for all the examined cases.

astro-ph.CO↗

arXiv2022

Neural Network Reconstruction of Late-Time Cosmology and Null Tests

The prospect of nonparametric reconstructions of cosmological parameters from observational data sets has been a popular topic in the literature for a number of years. This has mainly taken the form of a technique based on Gaussian processes but this approach is exposed to several foundational issues ranging from overfitting to kernel consistency problems. In this work, we explore the possibility of using artificial neural networks (ANN) to reconstruct late-time expansion and large scale structure cosmological parameters. We first show how mock data can be used to design an optimal ANN for both parameters, which we then use with real data to infer their respective redshift profiles. We further consider cosmological null tests with the reconstructed data in order to confirm the validity of the concordance model of cosmology, in which we observe a mild deviation with cosmic growth data.

astro-ph.CO↗

arXiv2022

Spatial Curvature in $f(R)$ Gravity

In this work, we consider four $f(R)$ gravity models -- the Hu-Sawicki, Starobinsky, Exponential and Tsujikawa models -- and use a range of cosmological data, together with Markov Chain Monte Carlo sampling techniques, to constrain the associated model parameters. Our main aim is to compare the results we get when $Ω_{k,0}$ is treated as a free parameter with their counterparts in a spatially flat scenario. The bounds we obtain for $Ω_{k,0}$ in the former case are compatible with a flat geometry. It appears, however, that a higher value of the Hubble constant $H_0$ allows for more curvature. Indeed, upon including in our analysis a Gaussian likelihood constructed from the local measurement of $H_0$, we find that the results favor an open universe at a little over $1σ$. This is perhaps not statistically significant, but it underlines the important implications of the Hubble tension for the assumptions commonly made about spatial curvature. We note that the late-time deviation of the Hubble parameter from its $Λ$CDM equivalent is comparable across all four models, especially in the non-flat case. When $Ω_{k,0}=0$, the Hu-Sawicki model admits a smaller mean value for $Ω_{\text{cdm},0}h^2$, which increases the said deviation at redshifts higher than unity. We also study the effect of a change in scale by evaluating the growth rate at two different wavenumbers $k_\dagger$. Any changes are, on the whole, negligible, although a smaller $k_\dagger$ does result in a slightly larger average value for the deviation parameter $b$.

astro-ph.CO↗

arXiv2021

Performance of Non-Parametric Reconstruction Techniques in the Late-Time Universe

In the context of a Hubble tension problem that is growing in its statistical significance, we reconsider the effectiveness of non-parametric reconstruction techniques which are independent of prescriptive cosmological models. By taking cosmic chronometers, Type Ia Supernovae and baryonic acoustic oscillation data, we compare and contrast two important reconstruction approaches, namely Gaussian processes (GP) and the \textbf{Lo}cally w\textbf{e}ighted \textbf{S}catterplot \textbf{S}moothing together with \textbf{Sim}ulation and \textbf{ex}trapolation method (LOESS-Simex or LS). In the context of these methods, besides not requiring a cosmological model, they also do not require physical parameters in their approach to their reconstruction of data (but they do depend on statistical hyperparameters). We firstly show how both GP and LOESS-Simex can be used to successively reconstruct various data sets to a high level of precision. We then directly compare both approaches in a quantitative manner by considering several factors, such as how well the reconstructions approximate the data sets themselves to how their respective uncertainties evolve. In light of the puzzling Hubble tension, it is important to consider how the uncertain regions evolve over redshift and the methods compare for estimating cosmological parameters at current times. For cosmic chronometers and baryonic acoustic oscillation compiled data sets, we find that GP generically produce smaller variances for the reconstructed data with a minimum value of $σ_{\rm GP-min} = 1.1$, while the situation for LS is totally different with a minimum of $σ_{\rm LS-min} = 50.8$. Moreover, some of these characteristics can be alleviate at low $z$, where LS presents less underestimation in comparison to GP.

astro-ph.CO↗

arXiv2021

Reconstructing teleparallel gravity with cosmic structure growth and expansion rate data

In this work, we use a combined approach of Hubble parameter data together with redshift-space-distortion $(fσ_8)$ data, which together are used to reconstruct the teleparallel gravity (TG) Lagrangian via Gaussian processes (GP). The adopted Hubble data mainly comes from cosmic chronometers, while for the Type Ia supernovae data we use the latest jointly calibrated Pantheon compilation. Moreover, we consider two main GP covariance functions, namely the squared-exponential and Cauchy kernels in order to show consistency (to within 1$σ$ uncertainties). The core results of this work are the numerical reconstructions of the TG Lagrangian from GP reconstructed Hubble and growth data. We take different possible combinations of the datasets and kernels to illustrate any potential differences in this regard. We show that nontrivial cosmology beyond $Λ$CDM falls within the uncertainties of the reconstructions from growth data, which therefore indicates no significant departure from the concordance cosmological model.

astro-ph.CO↗

arXiv2020

Cosmology with the Einstein Telescope: No Slip Gravity Model and Redshift Specifications

The Einstein Telescope and other third generation interferometric detectors of gravitational waves are projected to be operational post $2030$. The cosmological signatures of gravitational waves would undoubtedly shed light on any departure from the current gravitational framework. We here confront a specific modified gravity model, the No Slip Gravity model, with forecast observations of gravitational waves. We compare the predicted constraints on the dark energy equation of state parameters $w_0^{}-w_a^{}$, between the modified gravity model and that of Einstein gravity. We show that the No Slip Gravity model mimics closely the constraints from the standard gravitational theory, and that the cosmological constraints are very similar. The use of spectroscopic redshifts, especially in the low--redshift regime, lead to significant improvements in the inferred parameter constraints. We test how well such a prospective gravitational wave dataset would function at testing such models, and find that there are significant degeneracies between the modified gravity model parameters, and the cosmological parameters that determine the distance, due to the gravitational wave dimming effect of the modified theory.

astro-ph.CO↗

arXiv2020

Constraining Teleparallel Gravity through Gaussian Processes

We apply Gaussian processes (GP) in order to impose constraints on teleparallel gravity and its $f(T)$ extensions. We use available $H(z)$ observations from (i) cosmic chronometers data (CC); (ii) Supernova Type Ia (SN) data from the compressed Pantheon release together with the CANDELS and CLASH Multi-Cycle Treasury programs; and (iii) baryonic acoustic oscillation (BAO) datasets from the Sloan Digital Sky Survey. For the involved covariance functions, we consider four widely used choices, namely the square exponential, Cauchy, Matérn and rational quadratic kernels, which are consistent with one another within 1$σ$ confidence levels. Specifically, we use the GP approach to reconstruct a model-independent determination of the Hubble constant $H_0$, for each of these kernels and dataset combinations. These analyses are complemented with three recently announced literature values of $H_0$, namely (i) Riess $H_0^{\rm R} = 74.22 \pm 1.82 \,{\rm km\, s}^{-1} {\rm Mpc}^{-1}$; (ii) H0LiCOW Collaboration $H_0^{\rm HW} = 73.3^{+1.7}_{-1.8} \,{\rm km\, s}^{-1} {\rm Mpc}^{-1}$; and (iii) Carnegie-Chicago Hubble Program $H_0^{\rm TRGB} = 69.8 \pm 1.9 \,{\rm km\, s}^{-1} {\rm Mpc}^{-1}$. Additionally, we investigate the transition redshift between the decelerating and accelerating cosmological phases through the GP reconstructed deceleration parameter. Furthermore, we reconstruct the model-independent evolution of the dark energy equation of state, and finally reconstruct the allowed $f(T)$ functions. As a result, the $Λ$CDM model lies inside the allowed region at 1$σ$ in all the examined kernels and datasets, however a negative slope for $f(T)$ versus $T$ is slightly favored.

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