arXiv subjects

Savvas Nesseris

Publications and source records attributed to Savvas Nesseris.

At least 19 recordsLinked to original sources

arXiv2026

Constraining $f(R)$ gravity and evolving dark energy via large-scale structure and phase-space trajectories

We present a joint observational analysis confronting viable $f(R)$ modified gravity theories, specifically the Hu \& Sawicki and Starobinsky models, with background and large-scale structure (LSS) data. Utilizing Monte-Carlo Markov chain (MCMC) sampling across datasets including baryon acoustic oscillations (BAO), type Ia supernovae (SNeIa), cosmic microwave background (CMB) distance priors, and linear growth measurements ($fσ_8$, $f$, $σ_8$), we place tight constraints on the model parameters governing deviations from General Relativity. For the full dataset combination, we obtain $\log_{10} b_\mathrm{HS} = -6.325_{-1.138}^{+1.216}$ for the Hu \& Sawicki model and $b_\mathrm{S} = (0.8\pm61.0)\times10^{-4}$ for the Starobinsky model. Model comparison based on the Akaike Information Criterion indicates that these $f(R)$ extensions are statistically favored over flat $Λ\text{CDM}$ ($|Δ\text{AIC}| \ge 3.99$) for the combined data. However, when considering the Bayesian Information Criterion, the evidence for support is significantly reduced. Furthermore, we construct two-dimensional phase-space diagrams in the $(μ, γ)$ and $(μ, Σ)$ planes across several redshifts, establishing a novel diagnostic null-test allowing us to probe for deviations from $Λ\text{CDM}$, corresponding to the fixed point $(1,1)$ in both planes, using LSS observables. Should future weak-lensing and galaxy surveys provide data points with $μ-1<0$ and $γ-1>1$ or $Σ-1 < 0 $, then the aforementioned models could be directly ruled out.

physics.gen-ph↗

arXiv2026

GAME: Genetic Algorithms with Marginalised Ensembles for model-independent reconstruction of cosmological quantities

Genetic Algorithms (GA) are a powerful tool for stochastic optimisation and non-parametric symbolic regression, already widely used in cosmology. They are capable of reconstructing analytical functions directly from data points without introducing new physical models. A limitation of this approach is that while the reconstructed function is very efficient at reproducing the behaviour of the data points, non-observable quantities involving derivatives are particularly sensitive to stochasticity, hyperparameters, and to the choice of the best-fit function obtained by the GA, which implies the risk of the algorithm getting stuck in a local minimum. In this work we propose an update to the GA methodology for the reconstruction of analytical functions that involves computing a weighted average of an ensemble of GA configurations (GAME). We define the weights via a quantity that accounts for both the goodness-of-fit of the points and the smoothness of the resulting function. We also present a practical method to analytically estimate and correct the errors on the averaged function by combining a path-integral approach with an ensemble variance. We demonstrate the improvement offered by GAME methodology on a generic test function. We then apply the new methodology to a non-parametric reconstruction of the Hubble rate $H(z)$ using Cosmic Chronometers data and, assuming a flat Friedmann-Lemaître-Robertson-Walker background and General Relativity, we infer the corresponding dark energy equation of state $w(z)$. Through consistency tests, we show that current data produces results compatible with $Λ$CDM, and that Stage IV cosmology surveys will allow GA reinforced with GAME methodology to become an even more competitive tool for discriminating between different models.

astro-ph.CO↗

arXiv2026

To CPL, or not to CPL? What we have not learned about the dark energy equation of state

We show that using a Taylor expansion for the dark energy equation-of-state parameter and limiting it to the zeroth and first-order terms, i.e., the so-called Chevallier-Polarski-Linder (CPL) parametrization in regimes where it has been shown to fail as a physics-based two-parameter model, instead of allowing for higher-order terms and then marginalizing over them, adds extra information not present in the data and leads to markedly different and potentially misleading conclusions. Fixing the higher-order terms to zero, one concludes that vacuum energy that is currently non-dynamical (e.g., the cosmological constant) is excluded at several $σ$ significance as the explanation of cosmic acceleration, even in Dark Energy Spectroscopic Survey (DESI) DR1 data. Meanwhile, instead marginalizing over the higher-order terms shows that we know neither the current dark energy equation of state nor its current rate of change well enough to make such a claim. The CPL parametrization also implies that dark energy exhibits phantom behavior at high redshifts, while we show that by allowing the higher-order terms---which is required in order to capture the behavior of the dark energy equation of state in regimes beyond the validity of the CPL parametrization---the evidence for this phantom-like dark energy significantly weakens. This is not an argument for the higher-order phenomenological parametrizations, but rather a caution regarding such parametrizations in general. This issue has become more prominent now with the recent release of high-quality Stage IV galaxy survey data. The results of analyses using simple parametrizations should be interpreted with great care.

astro-ph.CO↗

arXiv2026

Exploring Hu-Sawicki-like modified gravity with Genetic Algorithms

We investigate whether viable Hu-Sawicki-like $f(R)$ models can produce deviations from $Λ\mathrm{CDM}$ that can be tested against current background cosmological data. We adopt a machine-learning approach based on Genetic Algorithms (GA) to reconstruct analytical perturbations around the Hu-Sawicki class of models. We develop a pipeline that interfaces the \texttt{GATO} GA code with the \texttt{CANDI} cosmology code. Each $f(R)$ function generated by the GA is first tested against theoretical viability conditions, including stability, the recovery of a standard matter-dominated epoch, the General Relativity limit, and chameleon screening mechanism. Viable candidates are then passed to \texttt{CANDI} to reconstruct the corresponding background cosmology and are tested against DESI DR2 BAO measurements and the Pantheon+ Type Ia supernova catalogue. %\newline The deviations we find are largest at late times, where the lower curvature makes modified-gravity effects more relevant, and are rapidly suppressed at higher redshift, in agreement with the imposed matching to the matter-dominated era. To further quantify deviations from the standard cosmological model, we compute the $Om(z)$ diagnostic. It shows only a very small departure from the constant $Λ\mathrm{CDM}$ behaviour. The effective dark energy equation of state associated with the reconstructed $f(R)$ function also evolves only weakly, showing a mild transition from an effective quintessence-like nature to an effective phantom-like regime. Overall, our results indicate that, within perturbations around the Hu-Sawicki class of models, current background data allow only limited deviations from $Λ\mathrm{CDM}$.

astro-ph.CO↗

arXiv2026

DESI constraints on two-field quintessence with exponential potentials

We investigate a quintessence model involving two scalar fields with double-exponential potentials. This configuration allows the system as a whole to emulate the dynamics of a single field with a shallower potential, enabling scalar fields that individually cannot drive cosmic acceleration to collectively achieve and sustain it. We assess the viability of this model by performing a fully Bayesian analysis and confronting its predictions with observational data, including the Planck 2018 cosmic microwave background (CMB) shift parameters, the newly released Dark Energy Spectroscopic Instrument (DESI) DR2 baryon acoustic oscillation (BAO) measurements, and the Dark Energy Survey Year 5 (DESY5) type Ia supernova (SnIa) sample. Our analysis shows that the two-field quintessence model yields a log Bayes factor relative to the flat $Λ$ cold dark matter model of $Δ\ln B \sim 4$, indicating moderate evidence against the latter. We also find that the central values of the two slopes of the exponential potentials are both close to 1, whereas the slope of an effective single-field system is constrained to be less than order unity. This property is theoretically desirable from the perspective of higher-dimensional theories. Thus, the two-field quintessence model with exponential potentials provides a physically motivated and compelling mechanism that is consistent with both observational and theoretical requirements.

astro-ph.CO↗

arXiv2026

A calibration-free null test from anisotropic BAO

Baryon acoustic oscillation (BAO) analyses usually report the anisotropic shift parameters $α_\perp(z)$ and $α_\parallel(z)$ relative to a fiducial cosmology, and these quantities are primarily used for cosmological parameter inference. Here we show that they can also be used to construct a direct internal consistency test of the background geometry. In particular, we derive a new null test of flat Friedmann-Lemaître-Robertson-Walker (FLRW) geometry written entirely in terms of the reported BAO shift parameters. The test is calibration free: the sound-horizon ratio $r_{\rm d}/r^{\rm fid}_{\rm d}$ cancels identically, so the relation is independent of the absolute BAO scale. We also derive a calibration-free reconstruction of the deceleration parameter $q(z)$ from the radial BAO sector. Applying these results to anisotropic DESI DR2 BAO measurements, we find no evidence for a breakdown of the flat-FLRW distance relation within current uncertainties. Our results show that anisotropic BAO measurements already provide a nontrivial internal geometric consistency test before performing any model fit.

astro-ph.CO↗

arXiv2026

Interpretable and physics-informed emulator for the linear matter power spectrum from machine learning

We present an interpretable emulator for the linear matter power spectrum (MPS) in the standard cosmological model $Λ$CDM, constructed via a physics-informed symbolic regression framework. By combining domain knowledge with a machine learning technique known as genetic algorithms, we explore the space of analytic expressions to derive closed-form, smooth, physically motivated approximations of the MPS that match the accuracy of standard broadband reconstruction methodologies such as the Savitzky-Golay filter. Building upon this baseline, we incorporate transparent oscillatory corrections informed by the physics of baryon acoustic oscillations (BAO). The resulting expression delivers mean sub-percent fractional errors across a broad range of scales ($k \in [10^{-5}, 1.5]~h\,\mathrm{Mpc}^{-1}$) with an average deviation of $\sim 0.4\%$ when tested against spectra computed with a Boltzmann solver. Moreover, a comparable level of fractional deviation is maintained on smaller scales when the GA-derived formulation is used as input to the nonlinear emulator halofit. To illustrate the versatility of the framework beyond $Λ$CDM, we apply it to a representative $f(R)$ gravity model. Rather than training a general modified-gravity emulator, we compute the corresponding linear spectra with a Boltzmann solver and fit a parametric deformation of the $Λ$CDM smoothed component. This procedure achieves average errors at the 1.5-1.8\% level and captures the leading modulation of the MPS induced by modified gravity, enabling a controlled study of its impact on the BAO scale. Our results provide compact, accurate, and physically motivated fitting functions for the linear MPS in both standard and MG cosmologies, offering a fast and transparent alternative to existing emulators for parameter inference and theoretical modeling in large-scale structure analyses.

astro-ph.CO↗

arXiv2026

Astrometric constraints on stochastic gravitational wave background with neural networks

Astrometric measurements provide a unique avenue for constraining the stochastic gravitational wave background (SGWB). In this work, we investigate the application of two neural network architectures, a fully connected network and a graph neural network, for analyzing astrometric data to detect the SGWB. Specifically, we generate mock Gaia astrometric measurements of the proper motions of sources and train two networks to predict the energy density of the SGWB, $Ω_\text{GW}$. We evaluate the performance of both models under varying input datasets to assess their robustness across different configurations. We also perform a direct comparison with a likelihood-based approach using Markov chain Monte Carlo (MCMC) methods, finding out that the neural-network-based approach is significantly faster, taking on the order of minutes, compared to MCMC's order of days, while still capturing the same features in the data. Our results demonstrate that neural networks can effectively constrain the SGWB, showing promise as tools for addressing systematic uncertainties and modeling limitations that pose challenges for traditional likelihood-based methods.

astro-ph.CO↗

arXiv2026

Forecast constraints on null tests of the $Λ$CDM model with SPHEREx

In this work we quantify the ability of the upcoming SPHEREx survey to constrain cosmological observables and test the internal consistency of the cosmological constant and cold dark matter ($Λ$CDM) model. Using Fisher matrix forecasting, we assess the expected precision on Baryon Acoustic Oscillations (BAO) observables, such as the angular diameter distance $D_\mathrm{A}(z)$ and the Hubble parameter $H(z)$. We further explore SPHEREx's potential to probe some of the fundamental assumptions of large-scale spatial homogeneity and isotropy, through model-independent reconstructions of several consistency tests of the $Λ$CDM model. In addition, we also examine the effect of the model dependence of the resulting Fisher and covariance matrices, using a neural network (NN) classification approach. We find that, while it is commonly assumed the covariance matrix depends weakly on the model, in fact the NN can very accurately ($\sim 98\%$) detect the underlying fiducial cosmological model based solely on the covariance matrix of the data, thus challenging this assumption. This model dependence, often neglected in standard analyses, can be naturally incorporated within simulation-based inference frameworks, which offer a flexible alternative for capturing such effects.

astro-ph.CO↗

arXiv2025

Probing the Distance Duality Relation with Machine Learning and Recent Data

The distance duality relation (DDR) relates two independent ways of measuring cosmological distances, namely the angular diameter distance and the luminosity distance. These can be measured with baryon acoustic oscillations (BAO) and Type Ia supernovae (SNe Ia), respectively. Here, we use recent DESI DR1, Pantheon+, SH0ES and DES-SN5YR data to test this fundamental relation. We employ a parametrised approach and also use model-independent Generic Algorithms (GA), which are a machine learning method where functions evolve loosely based on biological evolution. When we use DESI and Pantheon+ data without Cepheid calibration or big bang nucleosynthesis (BBN), there is a $2σ$ violation of the DDR in the parametrised approach. Then, we add high-redshift BBN data and the low-redshift SH0ES Cepheid calibration. This reflects the Hubble tension since both data sets are in tension in the standard cosmological model $Λ$CDM. In this case, we find a significant violation of the DDR in the parametrised case at $6σ$. Replacing the Pantheon+ SNe Ia data by DES-SN5YR, we find similar results. For the model-independent approach, we find no deviation in the uncalibrated case and a small deviation with BBN and Cepheids which remains at 1$σ$. This shows the importance of considering model-independent approaches for the DDR.

astro-ph.CO↗

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.

astro-ph.CO↗

arXiv2025

Has DESI detected exponential quintessence?

The new Dark Energy Spectroscopic Instrument (DESI) DR2 results have strengthened the possibility that dark energy is dynamical, i.e., it has evolved over the history of the Universe. One simple, but theoretically well motivated and widely studied, physical model of dynamical dark energy is minimally coupled, single-field quintessence $ϕ$ with an exponential potential $V(ϕ)=V_0\,e^{-λϕ}$. We perform a full Bayesian statistical analysis of the model using the DESI DR2 data, in combination with other cosmological observations, to constrain the model's parameters and to compare its goodness of fit to that of the standard $Λ$CDM model. We find that the quintessence model provides a significantly better fit to the data, both when the spatial curvature of the Universe is fixed to zero and when it is allowed to vary. The significance of the preference varies between $\sim3.3σ$ and $\sim3.8σ$, depending on whether the curvature density parameter $Ω_K$ is fixed or varied. We obtain the values $0.698^{+0.173}_{-0.202}$ and $0.722^{+0.182}_{-0.208}$ at the $68.3\%$ (i.e., $1σ$) confidence level for the parameter $λ$ in the absence and presence of $Ω_K$, respectively, which imply $\sim3.5σ$ preference for a nonzero $λ$. We also obtain $Ω_K=0.003\pm 0.001$, which implies $\sim3σ$ preference for a positive $Ω_K$, i.e., a negative curvature. Finally, we discuss the differences between quintessence and phenomenological parametrizations of the dark energy equation-of-state parameter, in particular the Chevallier-Polarski-Linder (CPL) parametrization, as well as a few caveats to our results.

astro-ph.CO↗

arXiv2025

DESI constraints on $α$-attractor inflationary models

The recent results on the baryon acoustic oscillations measurements from the DESI collaboration have shown tantalizing hints for a time-evolving dark energy equation of state parameter $w(z)$, with a statistically significant deviation from the cosmological constant and cold dark matter $Λ$CDM model. One of the simplest and theoretically well-motivated plausible candidates to explain the observed behavior of $w(z)$, is scalar-field quintessence. Here, we consider a class of models known as $α$-attractor, which describe in a single framework both inflation and the late-time acceleration of the Universe. Using the recent DESI data, in conjunction with other cosmological observations, we place stringent constraints on $α$-attractor models and compare them to the $Λ$CDM model. We find the $α$ parameter of the theory, which is physically motivated from supergravity and supersymmetry theories to have the values $3α\in \{1,2,3,4,5,6,7\}$, is constrained to be $α\simeq 1.89_{-0.35}^{+0.40}$. In addition, we find that the rest of the cosmological parameters of the model agree with the corresponding values of $Λ$CDM, while a Bayesian analysis finds strong support in favor of the $α$-attractor model. We also highlight an interesting connection between the $α$-attractor models and the stochastic gravitational wave background, where a contribution to the latter could derive from an enhancement of inflationary gravitational waves at high frequencies due to an early kination phase, thus providing an interesting alternative way to constrain the theory.

astro-ph.CO↗

arXiv2025

Enhancing Cosmological Model Selection with Interpretable Machine Learning

We propose a novel approach using neural networks (NNs) to differentiate between cosmological models, and implemented LIME as an interpretability approach to identify the key features influencing our model's decisions. We show the potential of NNs to enhance the extraction of meaningful information from cosmological large-scale structure data, based on current galaxy-clustering survey specifications, for the cosmological constant and cold dark matter ($Λ$CDM) model and the Hu-Sawicki $f(R)$ model. We find that the NN can successfully distinguish between $Λ$CDM and the $f(R)$ models, by predicting the correct model with approximately $97\%$ overall accuracy, thus demonstrating that NNs can maximize the potential of current and next generation surveys to probe for deviations from general relativity.

astro-ph.CO↗

arXiv2025

To curve, or not to curve: Is curvature-assisted quintessence observationally viable?

Single-field models of accelerated expansion with nearly flat potentials, despite being able to provide observationally viable explanations for the early-time cosmic inflation and the late-time cosmic acceleration, are in strong tension with string theory evidence and the associated de Sitter swampland constraints. It has recently been argued that in an open universe, where the spatial curvature is negative (i.e., with $Ω_k>0$), a new stable fixed point arises, which may lead to viable single-field-based accelerated expansion with an arbitrarily steep potential. Here, we show, through a dynamical systems analysis and a Bayesian statistical inference of cosmological parameters, that the additional cosmological solutions based on the new fixed point do not render steep-potential, single-field, accelerated expansion observationally viable. We mainly focus on quintessence models of dark energy, but we also argue that a similar conclusion can be drawn for cosmic inflation.

hep-th↗

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.

astro-ph.CO↗

arXiv2024

Mass octupole and current quadrupole corrections to gravitational wave emission from close hyperbolic encounters

In this paper, we study the next-to-leading order corrections in the mass multipole expansion, i.e. the mass octupole and current quadrupole, to gravitational wave production by close hyperbolic encounters of compact objects. We find that the signal is again, as in the simple quadrupole case, a burst event with the majority of the released energy occurring during the closest approach. In particular, we investigate the relative contribution to the power, both in the time and frequency domains, and total energy emitted by each order in the mass multipole expansion in gravitational waves. To do so, we include in the quadrupole term its first order post-Newtonian correction, giving this a contribution to the power of the same order as that of the mass octupole and the current quadrupole. We find specific configurations of systems where these corrections could be important and should be taken into account when analysing burst events.

gr-qc↗

arXiv2024

Outliers in DESI BAO: robustness and cosmological implications

We apply an Internal Robustness (iR) analysis to the recently released Dark Energy Spectroscopic Instrument (DESI) baryon acoustic oscillations dataset. This approach examines combinations of data subsets through a fully Bayesian model comparison, aiming to identify potential outliers, subsets possibly influenced by systematic errors, or hints of new physics. Using this approach, we identify three data points at $z= 0.295,\,0.51,\,0.71$ as potential outliers. Excluding these points improves the internal robustness of the dataset by minimizing statistical anomalies and enables the recovery of $Λ$CDM predictions with a best-fit value of $w_0 = -1.050 \pm 0.128$ and $w_a = 0.208 \pm 0.546$. These results raise the intriguing question of whether the identified outliers signal the presence of systematics or point towards new physics.

astro-ph.CO↗