arXiv subjects

Savvas Nesseris

Publications and source records attributed to Savvas Nesseris.

At least 55 records · Page 3Linked to original sources

arXiv2021

Search for black hole hyperbolic encounters with gravitational wave detectors

In recent years, the proposal that there is a large population of primordial black holes living in dense clusters has been gaining popularity. One natural consequence of these dense clusters will be that the black holes inside will gravitationally scatter off each other in hyperbolic encounters, emitting gravitational waves that can be observed by current detectors. In this paper we will derive how to compute the gravitational waves emitted by black holes in hyperbolic orbits, taking into account up to leading order spin effects. We will then study the signal these waves leave in the network of gravitational wave detectors currently on Earth. Using the properties of the signal, we will detail the data processing techniques that can be used to make it stand above the detector noise. Finally, we will look for these signals from hyperbolic encounters in the publicly available LIGO-Virgo data. For this purpose we will develop a two step trigger. The first step of the trigger will be based on looking for correlations between detectors in the time-frequency domain. The second step of the trigger will make use of a residual convolutional neural network, trained with the theoretical predictions for the signal, to look for hyperbolic encounters. With this trigger we find 8 hyperbolic encounter candidates in the 15.3 days of public data analyzed. Some of these candidates are promising, but the total number of candidates found is consistent with the number of false alarms expected from our trigger.

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arXiv2021

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

The Copernican principle (CP), i.e. the assumption that we are not privileged observers of the Universe, is a fundamental tenet of the standard cosmological model. A violation of this postulate implies the possibility that the apparent cosmic acceleration could be explained without the need of a cosmological constant, dark energy or paper we present a new test of the CP relating the distance and the expansion rate, derived via Noether's theorem, which is complementary to other tests found in the literature. We also simulate fiducial data based on upcoming stage IV galaxy surveys and use them to reconstruct the Hubble rate $H(z)$ and the angular diameter distance $d_A(z)$ in order to forecast how well our null test can constrain deviations from the cosmological constant model. We find that our new test can easily rule out several scenarios based on the Lema\^ıtre-Tolman-Bondi void model at confidence of $\gtrsim 3σ$ at middle to high redshifts ($z>0.5$).

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arXiv2021

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

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

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arXiv2021

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

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

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arXiv2021

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

A plethora of observational data obtained over the last couple of decades has allowed cosmology to enter into a precision era and has led to the foundation of the standard cosmological constant and cold dark matter paradigm, known as the $Λ$CDM model. Given the many possible extensions of this concordance model, we present here several novel consistency tests which could be used to probe for deviations from $Λ$CDM. First, we derive a joint consistency test for the spatial curvature $Ω_{k,0}$ and the matter density $Ω_\textrm{m,0}$ parameters, constructed using only the Hubble rate $H(z)$, which can be determined directly from observations. Second, we present a new test of possible deviations from homogeneity using the combination of two datasets, either the baryon acoustic oscillation (BAO) and $H(z)$ data or the transversal and radial BAO data, while we also introduce two consistency tests for $Λ$CDM which could be reconstructed via the transversal and radial BAO data. We then reconstruct the aforementioned tests using the currently available data in a model independent manner using a particular machine learning approach, namely the Genetic Algorithms. Finally, we also report on a $\sim 4σ$ tension on the transition redshift as determined by the $H(z)$ and radial BAO data.

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arXiv2021

Cosmological constraints with the Effective Fluid approach for Modified Gravity

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

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arXiv2021

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

We use simulated strongly lensed gravitational wave events from the Einstein Telescope to demonstrate how the luminosity and angular diameter distances, $d_L(z)$ and $d_A(z)$ respectively, can be combined to test in a model independent manner for deviations from the cosmic distance duality relation and the standard cosmological model. In particular, we use two machine learning approaches, the Genetic Algorithms and Gaussian Processes, to reconstruct the mock data and we show that both approaches are capable of correctly recovering the underlying fiducial model and can provide percent-level constraints at intermediate redshifts when applied to future Einstein Telescope data.

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arXiv2021

Strongly lensed supernovae as a self-sufficient probe of the distance duality relation

The observation of strongly lensed Type Ia supernovae enables both the luminosity and angular diameter distance to a source to be measured simultaneously using a single observation. This feature can be used to measure the distance duality parameter $η(z)$ without relying on multiple datasets and cosmological assumptions to reconstruct the relation between angular and luminosity distances. In this paper, we show how this can be achieved by future observations of strongly lensed Type Ia systems. Using simulated datasets, we reconstruct the function $η(z)$ using both parametric and non-parametric approaches, focusing on Genetic Algorithms and Gaussian processes for the latter. In the parametric approach, we find that in the realistic scenario of $N_{\rm lens}=20$ observed systems, the parameter $ε_0$ used to describe the trend of $η(z)$ can be constrained with the precision achieved by current SNIa and BAO surveys, while in the futuristic case ($N_{\rm lens}=1000$) these observations could be competitive with the forecast precision of upcoming LSS and SN surveys. Using the machine learning approaches of Genetic Algorithms and Gaussian processes, we find that both reconstruction methods are generally well able to correctly recover the underlying fiducial model in the mock data, even in the realistic case of $N_{\rm lens}=20$. Both approaches learn effectively from the features of the mock data points, yielding $1σ$ constraints that are in excellent agreement with the parameterised results.

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arXiv2021

Machine Learning and cosmographic reconstructions of quintessence and the Swampland conjectures

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

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arXiv2021

The clustering dynamics of primordial black boles in $N$-body simulations

We explore the possibility that Dark Matter (DM) may be explained by a non-uniform background of approximately stellar-mass clusters of Primordial Black Holes (PBHs), by simulating the evolution them from recombination to the present with over 5000 realisations using a Newtonian $ N $-body code. We compute the cluster rate of evaporation, and extract the binary and merged sub-populations along with their parent and merger tree histories, lifetimes and formation rates; the dynamical and orbital parameter profiles, the degree of mass segregation and dynamical friction, and power spectrum of close encounters. Overall, we find that PBHs can constitute a viable DM candidate, and that their clustering presents a rich phenomenology throughout the history of the Universe. We show that binary systems constitute about 9.5\% of all PBHs at present, with mass ratios of $ \bar{q}_{\rm B} = 0.154 $, and total masses of $ \bar{m}_{\rm T,\,B} = 303\,M_\odot$. Merged PBHs are rare, about 0.0023\% of all PBHs at present, with mass ratios of $ \bar{q}_{\rm B}= 0.965 $ with total and chirp masses of $ \bar{m}_{\rm T,\,B}= 1670\,M_\odot$ and $ \bar{m}_{c,{\rm M}} = 642\,M_\odot $ respectively. We find that cluster puffing up and evaporation leads to bubbles of these PBHs of order 1 kpc containing at present times about 36\% of objects and mass, with hundred pc sized cores. We also find that these PBH sub-haloes are distributed in wider PBH haloes of order hundreds of kpc, containing about 63\% of objects and mass, coinciding with the sizes of galactic halos. We find at last high rates of close encounters of massive Black Holes ($ M \sim 1000\,M_\odot$), with $ Γ^{\mathrm{S}} = (1.2^{+5.9}_{-0.9}) \times 10^{7} \mathrm{yr^{-1} Gpc^{-3}}$ and mergers with $Γ^{\mathrm{M}} = 1337 \pm 41 \mathrm{yr^{-1} Gpc^{-3}} $.

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arXiv2020

Constraints on the distance duality relation with standard sirens

We use gravitational wave (GW) standard sirens, in addition to Type Ia supernovae (SNIa) and baryon acoustic oscillation (BAO) mock data, to forecast constraints on the electromagnetic and gravitational distance duality relations (DDR). We make use of a parameterised approach based on a specific DDR violation model, along with a machine learning reconstruction method based on the Genetic Algorithms. We find that GW provide an alternative to the use of BAO data to constrain violations of the DDR, reaching $3\%$ constraints on the violation parameter we consider when combined with SNIa, which is only improved by a factor of $\approx1.4$ if one instead considers the combination of BAO and SNIa. We also investigate the possibility that a neglected modification of gravity might lead to a false detection of DDR violations, even when screening mechanisms are active. We find that such a false detection can be extremely significant, up to $\approx10σ$ for very extreme modified gravity scenarios, while this reduces to $\approx4σ$ in a more realistic case. False detections can also provide a smoking gun for the modified gravity mechanism at play, as a result of the tension introduced between the SNIa+GW and SNIa+BAO combinations.

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arXiv2020

Hints of dark energy anisotropic stress using Machine Learning

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

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arXiv2020

Cosmological constraints on non-adiabatic dark energy perturbations

The exact nature of dark energy is currently unknown and its cosmological perturbations, when dark energy is assumed not to be the cosmological constant, are usually modeled as adiabatic. Here we explore the possibility that dark energy might have a nonadiabatic component and we examine how it would affect several key cosmological observables. We present analytical solutions for the growth rate and growth index of matter density perturbations and compare them to both numerical solutions of the fluid equations and an implementation in the Boltzmann code CLASS, finding that they all agree to well below one percent. We also perform a Monte Carlo analysis to derive constraints on the parameters of the nonadiabatic component using the latest cosmological data, including the temperature and polarization spectra of the Cosmic Microwave Background as observed by Planck, the Baryon Acoustic Oscillations, the Pantheon type Ia supernovae compilation and lastly, measurements of Redshift Space Distortions (RSD) of the growth rate of matter perturbations. We find that the amplitude of the nonadiabatic pressure perturbation is consistent with zero within $1σ$. Finally, we also present a new, publicly available, RSD likelihood for MontePython based on the "Gold 2018" growth rate data compilation.

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arXiv2020

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

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

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arXiv2020

Is there any measurable redshift dependence on the SN Ia absolute magnitude?

We test the cosmological implications of a varying absolute magnitude of Type Ia supernovae using the Pantheon compilation, by reconstructing different phenomenological approaches that could justify a varying absolute magnitude, but also approaches based on cosmic voids, modified gravity models and a binning scheme. In all the cases considered in this work, we find good agreement with the expected values of the standard $Λ$CDM model and no evidence of new physics.

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arXiv2019

Evaporating primordial black holes as varying dark energy

If light enough primordial black holes (PBH) account for dark matter, then its density decreases with time as they lose mass via Hawking radiation. We show that this time-dependence of the matter density can be formulated as an equivalent $w(z)$ dark energy model and we study its implications on the expansion history. Using our approach and comparing with the latest cosmological data, including the supernovae type Ia, Baryon Acoustic Oscillations, Cosmic Microwave Background and the Hubble expansion H(z) data, we place observational constraints on the PBH model. We find that it is statistically consistent with $Λ$CDM according to the AIC statistical tool. Furthermore, we entertain the idea of having a population of ultra-light PBHs, decaying around neutrino decoupling, on top of the dark matter fluid and show how this offers a natural dark matter-radiation coupling altering the expansion history of the Universe and alleviating the $H_0$ tension.

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arXiv2019

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

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

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arXiv2019

Designing Horndeski and the effective fluid approach

We present a family of designer Horndeski models, i.e. models that have a background exactly equal to that of the $Λ$CDM model but perturbations given by the Horndeski theory. Then, we extend the effective fluid approach to Horndeski theories, providing simple analytic formulae for the equivalent dark energy effective fluid pressure, density and velocity. We implement the dark energy effective fluid formulae in our code EFCLASS, a modified version of the widely used Boltzmann solver CLASS, and compare the solution of the perturbation equations with those of the code hi_CLASS which already includes Horndeski models. We find that our simple modifications to the vanilla code are accurate to the level of $\sim 0.1\%$ with respect to the more complicated hi_CLASS code. Furthermore, we study the kinetic braiding model both on and off the attractor and we find that even though the full case has a proper $Λ$CDM model limit for large $n$, it is not appropriately smooth, thus causing the quasistatic approximation to break down. Finally, we focus on our designer model (HDES), which has both a smooth $Λ$CDM limit and well-behaved perturbations, and we use it to perform Markov Chain Monte Carlo analyses to constrain its parameters with the latest cosmological data. We find that our HDES model can also alleviate the soft $2σ$ tension between the growth data and Planck 18 due to a degeneracy between $σ_8$ and one of its model parameters that indicates the deviation from the $Λ$CDM model.

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