Testing Modified Gravity theory (MOG) with Type Ia Supernovae, Cosmic Chronometers and Baryon Acoustic Oscillations

Negrelli, Carolina Soledad; Kraiselburd, Lucila; Landau, Susana J.; Scóccola, Claudia G.

doi:10.1088/1475-7516/2020/07/015

Cited by 11 publications

(7 citation statements)

References 86 publications

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“…Our estimated nuissance parameters are consistent with those computed by the Pantheon compilation within 1σ. This agreement has been also obtained in a similar analysis carried out assuming another alternative theory of gravity [58], and in [56] where extensions of the ΛCDM models where assumed. All those mentioned analyses confirm that the value of the nuissance parameters are independent of the cosmological model.…”

Section: B Supernovae Type Iasupporting

confidence: 81%

Testing f(R) gravity models with quasar X-ray and UV fluxes

Leizerovich,

Kraiselburd,

Landau

et al. 2021

Preprint

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Section: B Supernovae Type Iasupporting

confidence: 81%

Testing f(R) gravity models with quasar X-ray and UV fluxes

Leizerovich,

Kraiselburd,

Landau

et al. 2021

Preprint

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“…Direct detection rates of relic neutrinos through neutrino capture on nuclei, as discussed in section 4, are not strongly sensitive to the incoming non-relativistic momentum of the captured neutrinos. This behavior follows from the product of the capture cross section times velocity being constant for neutrino kinetic energies below approximately 1 keV [11]. Therefore, starting from a cross-spectrum of the angular power for comoving neutrino momenta q 1 and q 2 for a given neutrino species, we have…”

Section: Jcap06(2021)053mentioning

confidence: 99%

Multi-messenger astrophysics with the cosmic neutrino background

Tully

Zhang

2021

J. Cosmol. Astropart. Phys.

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The massive neutrinos of the Cosmic Neutrino Background (CνB) are fundamental ingredients of the radiation-dominated early universe and are important non-relativistic probes of the large-scale structure formation in the late universe. The dominant source of anisotropies in the neutrino flux distribution on the sky are highly amplified integrals of metric perturbations encountered during the non-relativistic phase of the CνB. This paper numerically compares the line-of-sight methods for computing CνB anisotropies with the Einstein-Boltzmann hierarchy solutions in linear theory for a range of neutrino masses. Angular power spectra are computed that are relevant to a future polarized tritium target run of the PTOLEMY experiment. Correlations between the CνB sky maps and galactic survey data are derived using line-of-sight techniques and discussed in the context of multi-messenger astrophysics.

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“…In this work, we use the mean values estimated by the Pantheon compilation [24] (α = 0.0154 ± 0.06, β = 3.02 ± 0.06, γ = 0.053 ± 0.009) where a ΛCDM model is assumed. These values have been verified by two independent analyses with alternative cosmological models: the one in [51] where the f (R) Hu-Sawicky model is assumed and the one in [52] performed in the context of the MOdified Gravity theory (MOG). To perform the statistical analysis, we compare the value of µ obtained from the data using Eq.…”

Section: B Supernovae Iamentioning

confidence: 76%

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

Chantada¹,

Landau²,

Protopapas³

et al. 2022

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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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Testing Modified Gravity theory (MOG) with Type Ia Supernovae, Cosmic Chronometers and Baryon Acoustic Oscillations

Cited by 11 publications

References 86 publications

Testing f(R) gravity models with quasar X-ray and UV fluxes

Testing f(R) gravity models with quasar X-ray and UV fluxes

Multi-messenger astrophysics with the cosmic neutrino background

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

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