Dark Energy: back to Newton?

Lucy Calder; Ofer Lahav

doi:10.1111/j.1468-4004.2008.49113.x

Home/Archive/arXiv

arXiv · 0712.2196

Dark Energy: back to Newton?

Lucy Calder·Ofer Lahav

View original source ↗Read source document ↗Export metadata ↓

Abstract

Dark Energy is currently one of the biggest mysteries in science. In this article the origin of the concept is traced as far back as Newton and Hooke in the seventeenth century. Newton considered, along with the inverse square law, a force of attraction that varies linearly with distance. A direct link can be made between this term and Einstein's cosmological constant, Lambda, and this leads to a possible relation between Lambda and the total mass of the universe. Mach's influence on Einstein is discussed and the convoluted history of Lambda throughout the last ninety years is coherently presented.

Explore related subjects

astro-ph

Keep this discovery

BibTeX RIS

Lucy Calder, Ofer Lahav. 2007-12-13. Dark Energy: back to Newton?. https://doi.org/10.1111/j.1468-4004.2008.49113.x

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2009

Cosmology from very high energy $γ$-rays

In this work we study how the cosmological parameter, the Hubble constant $H_0$, can be constrained by observation of very high energy (VHE) $γ$-rays at the TeV scale. The VHE $γ$-rays experience attenuation by background radiation field through $e^+e^-$ pair production during the propagation in the intergalactic space. This effect is proportional to the distance that the VHE $γ$-rays go through. Therefore the absorption of TeV $γ$-rays can be taken as cosmological distance indicator to constrain the cosmological parameters. Two blazars Mrk 501 and 1ES 1101-232, which have relatively good spectra measurements by the atmospheric Cerenkov telescope, are studied to constrain $H_0$. The mechanism constraining the Hubble constant adopted here is very different from the previous methods such as the observations of type Ia supernovae and the cosmic microwave background. However, at $2σ$ level, our result is consistent with other methods.

astro-ph↗

arXiv2009

Improved optical mass tracer for galaxy clusters calibrated using weak lensing measurements

We develop an improved mass tracer for clusters of galaxies from optically observed parameters, and calibrate the mass relation using weak gravitational lensing measurements. We employ a sample of ~ 13,000 optically-selected clusters from the Sloan Digital Sky Survey (SDSS) maxBCG catalog, with photometric redshifts in the range 0.1-0.3. The optical tracers we consider are cluster richness, cluster luminosity, luminosity of the brightest cluster galaxy (BCG), and combinations of these parameters. We measure the weak lensing signal around stacked clusters as a function of the various tracers, and use it to determine the tracer with the least amount of scatter. We further use the weak lensing data to calibrate the mass normalization. We find that the best mass estimator for massive clusters is a combination of cluster richness, N_{200}, and the luminosity of the brightest cluster galaxy, L_{BCG}: M_{200\barρ} = (1.27 +/- 0.08) (N_{200}/20)^{1.20 +/- 0.09} (L_{BCG}/\bar{L}_{BCG}(N_{200}))^{0.71 +/- 0.14} \times 10^{14} h^{-1} M_sun, where $\bar{L}_{BCG}(N_{200})$ is the observed mean BCG luminosity at a given richness. This improved mass tracer will enable the use of galaxy clusters as a more powerful tool for constraining cosmological parameters.

astro-ph↗

arXiv2009

Bayesian reconstruction of the cosmological large-scale structure: methodology, inverse algorithms and numerical optimization

We address the inverse problem of cosmic large-scale structure reconstruction from a Bayesian perspective. For a linear data model, a number of known and novel reconstruction schemes, which differ in terms of the underlying signal prior, data likelihood, and numerical inverse extra-regularization schemes are derived and classified. The Bayesian methodology presented in this paper tries to unify and extend the following methods: Wiener-filtering, Tikhonov regularization, Ridge regression, Maximum Entropy, and inverse regularization techniques. The inverse techniques considered here are the asymptotic regularization, the Jacobi, Steepest Descent, Newton-Raphson, Landweber-Fridman, and both linear and non-linear Krylov methods based on Fletcher-Reeves, Polak-Ribiere, and Hestenes-Stiefel Conjugate Gradients. The structures of the up-to-date highest-performing algorithms are presented, based on an operator scheme, which permits one to exploit the power of fast Fourier transforms. Using such an implementation of the generalized Wiener-filter in the novel ARGO-software package, the different numerical schemes are benchmarked with 1-, 2-, and 3-dimensional problems including structured white and Poissonian noise, data windowing and blurring effects. A novel numerical Krylov scheme is shown to be superior in terms of performance and fidelity. These fast inverse methods ultimately will enable the application of sampling techniques to explore complex joint posterior distributions. We outline how the space of the dark-matter density field, the peculiar velocity field, and the power spectrum can jointly be investigated by a Gibbs-sampling process. Such a method can be applied for the redshift distortions correction of the observed galaxies and for time-reversal reconstructions of the initial density field.

astro-ph↗