Low-redshift formula for the luminosity distance in a LTB model with cosmological constant

Romano, Antonio Enea; Chen, Pisin

doi:10.1140/epjc/s10052-014-2780-z

Cited by 6 publications

(10 citation statements)

References 22 publications

(27 reference statements)

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“…The probability distribution of the δ field of the test set, and the corresponding reconstructed δ obtained applying the neural network to the luminosity distance are shown in fig. (3). As it can be seen the reconstructed δ follows approximately the same distribution of the test set, showing that the neural network is able to recover the statistical properties of the test data set.…”

Section: Results Of the Inversion Of The Density Fieldmentioning

confidence: 53%

“…Beside being useful to determine the background cosmological model parameters, they can also be used to reconstruct the density and peculiar velocity fields [2], providing a unique tool to probe large scale structure at scales where other astrophysical objects are too dim to be observed. In the context of the luminosity distance the inversion problem has been only solved assuming spherical symmetry [3], and it consisted of solving complicated systems of differential equations, which required smooth functions as inputs, but observational data is rarely in a smooth form, limiting the accuracy of the results. In this paper we will develop a completely new inversion method, which does not assume any symmetry.…”

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confidence: 99%

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Deep learning reconstruction of the large scale structure of the Universe from luminosity distance observations

García¹,

Santa²,

Romano³

2021

Preprint

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Supernovae Ia (SN) are among the brightest objects we can observe and can provide a unique window on the large scale structure of the Universe at redshifts where other observations are not available. The photons emitted by SNe are in fact affected by the density field between the source and the observer, and from the observed luminosity distance it is possible to solve the inversion problem (IP), i.e. to reconstruct the density field which produced those effects.So far the IP was only solved assuming some restrictions about the geometry of the problem, such as spherical symmetry for example, and the approach was based on solving complicated systems of differential equations which required smooth function as inputs, while observational data is not smooth, due to its discrete nature. In order to overcome these limitations we develop for the first time an inversion method which is not assuming any symmetry, and can be applied directly to observational data, without the need of any data smoothing procedure.The method is based on the use of convolutional neural networks (CNN) trained on simulated data, and it shows quite accurate results. The training data set is obtained by first generating random density and velocity profiles, and then computing their effects on the luminosity distance. The CNN is then trained to reconstruct the density field from the luminosity distance. The CNN is a modified version of U-Net to account for the tridimensionality of the data, and can reconstruct the density and velocity fields with a good level of accuracy.The use of neural networks to analyze observational data from future SNe catalogues will allow to reconstruct the large scale structure of the Universe to an unprecedented level of accuracy, at a redshift at which few other observations are available.

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Section: Results Of the Inversion Of The Density Fieldmentioning

confidence: 53%

mentioning

confidence: 99%

Deep learning reconstruction of the large scale structure of the Universe from luminosity distance observations

García¹,

Santa²,

Romano³

2021

Preprint

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“…However, we expect this to yield a subdominant correction to our results, since the cosmology dependence in the conversion to physical distances is very weak (see section 7.1 in[52]). Moreover, the only difference with respect to ΛLTB models arises from the curvature term[53], which is very small for the rather shallow density fluctuations that we consider. We have also explicitly verified that our results do not significantly change when employing the Pantheon data products without peculiar velocities corrections[52], which were released shortly after our analysis was completed.…”

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confidence: 95%

A cosmological underdensity does not solve the Hubble tension

Castello

Högås

Mörtsell

2022

J. Cosmol. Astropart. Phys.

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A potential solution to the Hubble tension is the hypothesis that the Milky Way is located near the center of a matter underdensity. We model this scenario through the Lemaître-Tolman-Bondi formalism with the inclusion of a cosmological constant (ΛLTB) and consider a generalized Gaussian parametrization for the matter density profile. We constrain the underdensity and the background cosmology with a combination of data sets: the Pantheon Sample of type Ia supernovae (both the full catalogue and a redshift-binned version of it), a collection of baryon acoustic oscillations data points and the distance priors extracted from the latest Planck data release. The analysis with the binned supernovae suggests a preference for a -13 % density drop with a size of approximately 300 Mpc, interestingly matching the prediction for the so-called KBC void already identified on the basis of independent analyses using galaxy distributions. The constraints obtained with the full Pantheon Sample are instead compatible with a homogeneous cosmology and we interpret this radically different result as a cautionary tale about the potential bias introduced by employing a binned supernova data set. We quantify the level of improvement on the Hubble tension by analyzing the constraints on the B-band absolute magnitude of the supernovae, which provides the calibration for the local measurements of H 0. Since no significant difference is observed with respect to an analogous fit performed with a standard ΛCDM cosmology, we conclude that the potential presence of a local underdensity does not resolve the tension and does not significantly degrade current supernova constraints on H 0.

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“…The use of the exact expression for t (η, r ) improves the accuracy for the expansion for the geodesics with respect to previous calculations [23], which were based on a perturbative expansion of t 0 (r ), rather than the use of the exact value. Now we can find the low redshift Taylor expansion for the geodesic equations [19], and then we calculate the Hubble parameter.…”

Section: Low-redshift Expansion Of the Hubble Parameter H(z)mentioning

confidence: 90%

“…The effects of a local inhomogeneity on cosmological observations have been studied already for different cases [1, such as the equation of state of dark energy or the luminosity distance [18,19,23]. It has been shown for example that the value of the cosmological constant could be affected significantly by the presence of local inhomogeneity seeded by primordial curvature perturbations [9], which could also lead to the wrong conclusion of a varying equation of state for dark energy while only a cosmological constant is present [19].…”

Section: Introductionmentioning

confidence: 99%

Low-redshift effects of local structure on the Hubble parameter in presence of a cosmological constant

Romano

Vallejo

2016

Eur. Phys. J. C

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In order to estimate the effects of a local structure on the Hubble parameter we calculate the low-redshift expansion for H (z) and δ H H for an observer at the center of a spherically symmetric matter distribution in the presence of a cosmological constant. We then test the accuracy of the formulas comparing them with fully relativistic nonperturbative numerical calculations for different cases for the density profile. The low-redshift expansion we obtain gives results more precise than perturbation theory since it is based on the use of an exact solution of Einstein's field equations. For larger density contrasts the low-redshift formulas accuracy improves respect to the perturbation theory accuracy because the latter is based on the assumption of a small density contrast, while the former does not rely on such an assumption. The formulas can be used to take into account the effects on the Hubble expansion parameter due to the monopole component of the local structure. If the H (z) observations will show deviations from the C DM prediction compatible with the formulas we have derived, this could be considered an independent evidence of the existence of a local inhomogeneity, and the formulas could be used to determine the characteristics of this local structure.

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Low-redshift formula for the luminosity distance in a LTB model with cosmological constant

Cited by 6 publications

References 22 publications

Deep learning reconstruction of the large scale structure of the Universe from luminosity distance observations

Deep learning reconstruction of the large scale structure of the Universe from luminosity distance observations

A cosmological underdensity does not solve the Hubble tension

Low-redshift effects of local structure on the Hubble parameter in presence of a cosmological constant

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