Spatial curvature in<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>f</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:mi>R</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:math>gravity

Farrugia, Christine; Sultana, Joseph; Mifsud, Jurgen

doi:10.1103/physrevd.104.123503

Cited by 15 publications

(6 citation statements)

References 96 publications

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“…[51], where data from six systems of strongly lensed quasars analyzed by the H0LiCOW Collaboration [73] were added to the previous ones (CC þ SNIa). On the other hand, another recent work [52] also tested the fðRÞ Hu-Sawicki model considering besides the CC and SNIa data, redshift-space distortions (RSD), BAO, and CMB data. We note that the resulting values of the b parameter were more restricted than in this work, but this can be explained since the former analysis considered more data sets than ours.…”

Section: Resultsmentioning

confidence: 99%

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Cosmology-informed neural networks to solve the background dynamics of the Universe

et al. 2023

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Section: Resultsmentioning

confidence: 99%

“…For example, in Refs. [50][51][52] the output of the numerical solvers in the region close to b ¼ 0 is replaced by the solution provided by the aforementioned method.…”

Section: B F ðRþ Gravity Implementationmentioning

confidence: 99%

Cosmology-informed neural networks to solve the background dynamics of the Universe

et al. 2023

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“…Furthermore, we also note that our estimates for the b parameter are in agreement within 1σ with the ones of another analysis performed in [62], where data from six systems of strongly lensed quasars analyzed by the HOLICOW Collaboration [67] are added to the previous ones (CC+SNeIa). On the other hand, another recent work [68] also tested the f (R) Hu-Sawicky model considering besides the CC and SNeIa data, Redshift Space Distortions (RSD) and CMB data. We note that the resulting values of the b parameter are more restricted than in this work, but this can be explained since the former analysis considers more data sets than ours.…”

Section: Resultsmentioning

confidence: 99%

Cosmology-informed neural networks to solve the background dynamics of the Universe

Chantada¹,

Landau²,

Protopapas³

et al. 2022

Preprint

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The field of machine learning has drawn increasing interest from various other fields due to the success of its methods at solving a plethora of different problems. An application of these has been to train artificial neural networks to solve differential equations without the need of a numerical solver. This particular application offers an alternative to conventional numerical methods, with advantages such as lower memory required to store the solutions, parallelization, and in some cases less overall computational cost than its numerical counterparts. In this work, we train artificial neural networks to represent a bundle of solutions of the differential equations that govern the background dynamics of the Universe for four different models. The models we have chosen are ΛCDM, the Chevallier-Polarski-Linder parametric dark energy model, a quintessence model with an exponential potential, and the Hu-Sawicki f (R) model. We used the solutions that the networks provide to perform statistical analyses to estimate the values of each model's parameters with observational data; namely, estimates of the Hubble parameter from Cosmic Chronometers, the Supernovae type Ia data from the Pantheon compilation, and measurements from Baryon Acoustic Oscillations. The results we obtain for all models match similar estimations done in the literature using numerical solvers. In addition, we estimated the error of the solutions by comparing them to the analytical solution when there is one, or to a high-precision numerical solution when there is not. Through those estimations we found that the error of the solutions was at most ∼ 1% in the region of the parameter space that concerns the 95% confidence regions that we found using the data, for all models and all statistical analyses performed in this work.

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“…Where λ, R c and n are dimensionless positive free parameters. This model includes the ΛCDM as a limit and could be seen as a late modification of the ΛCDM model [80]. Then, by assuming barotropic EoS defined by the Equations ( 26) and ( 27) one could derive the energy density for Hu-Sawicki model of gravitation, but in the current article we will only show the numerical solutions because of the fact that expressions for energy density are too big.…”

Section: Exponential F (R) Gravitymentioning

confidence: 99%

Generalised Ellis-Bronnikov Wormholes in $f(R)$ Gravity

Sokoliuk,

Mandal,

Sahoo

et al. 2022

Preprint

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In this manuscript, we construct generalized Ellis-Bronnikov wormholes in the context of f (R) modified theories of gravity. We consider that the matter driving the wormhole satisfies the energy conditions so that it is the effective energy-momentum tensor containing the higher-order derivatives of curvature terms that violate the null energy condition. Thus, the gravitational fluid is interpreted by the higher-order derivatives of curvature terms to represent the wormhole geometries and is fundamentally different from its counter representation in general relativity. In particular, we explore the wormhole geometries by presuming various well-known forms of Lagrangian f (R). In addition, for the seek of completeness, we discuss modified Tolman-Oppenheimer-Volkov, volume integral quantifier, and total gravitational energy.

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Spatial curvature inf(R)gravity

Cited by 15 publications

References 96 publications

Cosmology-informed neural networks to solve the background dynamics of the Universe

Cosmology-informed neural networks to solve the background dynamics of the Universe

Cosmology-informed neural networks to solve the background dynamics of the Universe

Generalised Ellis-Bronnikov Wormholes in $f(R)$ Gravity

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