Interplay between Swampland and Bayesian Machine Learning in constraining cosmological models

Elizalde, E.; Khurshudyan, Martiros

doi:10.1140/epjc/s10052-021-09130-8

Cited by 5 publications

(2 citation statements)

References 53 publications

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“…It is clear that the adopted strategy provides a highly modeldependent analysis, and that it would be desirable to perform the estimations using a model-independent method. Some of the above mentioned results can be found in [4][5][6][7][8][9][10][11][12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Swampland criteria for f(R) gravity derived with a Gaussian process

Elizalde

Khurshudyan

2022

Eur. Phys. J. C

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A Gaussian Process (GP) is used to derive Swampland criteria for f(R) modifications of General Relativity (GR). The fact that observational data are directly taken into account allows obtaining clean upper and lower limits for the Swampland criteria, in an unbiased, natural way. They correspond to a dark-energy dominated Universe, having assumed the form of the f(R) gravity, only. To perform this study, 40-point H(z) data are used, consisting of 30-point samples deduced from the differential age method, and 10-point additional samples coming from the radial BAO method. The constraints obtained for each f(R) model parameter choice indicate whether it is possible to alleviate the $$H_{0}$$ H 0 tension problem efficiently due to the used $$H_{0}$$ H 0 values reported by the Planck and Hubble missions. The elaborated structure of the analysis forced to limit the number of specific models, but the methodology here discussed is applicable to study any form of f(R) gravity.

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Section: Introductionmentioning

confidence: 99%

Swampland criteria for f(R) gravity derived with a Gaussian process

Elizalde

Khurshudyan

2022

Eur. Phys. J. C

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“…In the context of quintessence, the Swampland conjectures have been invoked in [6,[63][64][65][66][67][68], and JCAP10(2023)039 in other late-time model-specific contexts in [69][70][71][72][73][74][75]. Instead, analyses of the implications of the Swampland conjectures have also been made for the low-redshift universe in a more model-independent way in [76][77][78][79][80][81]. The general conclusion of these works is a mild but not insurmountable tension between late time observations of dark energy and the Swampland criteria.…”

Section: Introductionmentioning

confidence: 99%

News from the Swampland — constraining string theory with astrophysics and cosmology

Schöneberg,

Vacher,

Dias

et al. 2023

J. Cosmol. Astropart. Phys.

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Our current best guess for a unified theory of gravitation and quantum field theory (string theory) generically predicts a set of requirements for a consistently quantized theory, the Swampland criteria. Refined versions of these criteria have recently been shown to be in mild tension with cosmological observations. We summarize the status of the current impact of and constraints on the Swampland conjectures from cosmology, and subject a variety of dark energy quintessence models to recently released cosmological datasets. We find that instead of tightening the tension, the new data allows for slightly more freedom in the Swampland criteria. We further demonstrate that if there is no theoretical argument made to prevent interactions of the moduli fields with the electromagnetic sector, a novel fine-tuning argument arises from the extremely tight current constraints on such interactions. Finally, we conclude with a cautionary tale on model-independent reconstructions of the Swampland criteria from expansion rate data.

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Can we learn from matter creation to solve the $$H_{0}$$ tension problem?

Elizalde,

Khurshudyan,

Odintsov

2024

Eur. Phys. J. C

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The $$H_{0}$$ H 0 tension problem is studied in the light of a matter creation mechanism (an effective approach to replacing dark energy), the way to define the matter creation rate being of pure phenomenological nature. Bayesian (probabilistic) Machine Learning is used to learn the constraints on the free parameters of the models, with the learning being based on the generated expansion rate, H(z). Taking advantage of the method, the constraints for three redshift ranges are learned. Namely, for the two redshift ranges: $$z\in [0,2]$$ z ∈ [ 0 , 2 ] (cosmic chronometers) and $$z\in [0,2.5]$$ z ∈ [ 0 , 2.5 ] (cosmic chronometers + BAO), covering already available H(z) data, to validate the learned results; and for a third redshift interval, $$z\in [0,5]$$ z ∈ [ 0 , 5 ] , for forecasting purposes. It is learned that the $$3\alpha H_{0}$$ 3 α H 0 term in the creation rate provides options that have the potential to solve the $$H_{0}$$ H 0 tension problem.

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Interplay between Swampland and Bayesian Machine Learning in constraining cosmological models

Cited by 5 publications

References 53 publications

Swampland criteria for f(R) gravity derived with a Gaussian process

Swampland criteria for f(R) gravity derived with a Gaussian process

News from the Swampland — constraining string theory with astrophysics and cosmology

Can we learn from matter creation to solve the $$H_{0}$$ tension problem?

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