Galaxies near distant quasars: Evidence for statistical gravitational lensing

2005 ◽  
pp. 431-434
Author(s):  
Winfried Fugmann
Keyword(s):  
Gravitational Lensing

Nonlocal Gravity

2017 ◽  
Author(s):  
Bahram Mashhoon
Keyword(s):  
Gravitational Field ◽  
Gravitational Lensing ◽  
Past History ◽  
Lorentz Invariance ◽  
Minkowski Spacetime ◽  
Field Measurements ◽  
Local Principle ◽  
History Of ◽  
Expansion Of The Universe ◽  
Nearby Galaxies

A postulate of locality permeates through the special and general theories of relativity. First, Lorentz invariance is extended in a pointwise manner to actual, namely, accelerated observers in Minkowski spacetime. This hypothesis of locality is then employed crucially in Einstein’s local principle of equivalence to render observers pointwise inertial in a gravitational field. Field measurements are intrinsically nonlocal, however. To go beyond the locality postulate in Minkowski spacetime, the past history of the accelerated observer must be taken into account in accordance with the Bohr-Rosenfeld principle. The observer in general carries the memory of its past acceleration. The deep connection between inertia and gravitation suggests that gravity could be nonlocal as well and in nonlocal gravity the fading gravitational memory of past events must then be taken into account. Along this line of thought, a classical nonlocal generalization of Einstein’s theory of gravitation has recently been developed. In this nonlocal gravity (NLG) theory, the gravitational field is local, but satisfies a partial integro-differential field equation. A significant observational consequence of this theory is that the nonlocal aspect of gravity appears to simulate dark matter. The implications of NLG are explored in this book for gravitational lensing, gravitational radiation, the gravitational physics of the Solar System and the internal dynamics of nearby galaxies as well as clusters of galaxies. This approach is extended to nonlocal Newtonian cosmology, where the attraction of gravity fades with the expansion of the universe. Thus far only some of the consequences of NLG have been compared with observation.

Inclination Effects in Spiral Galaxy Gravitational Lensing

1997 ◽  
Vol 486 (2) ◽  
pp. 681-686 ◽  
Author(s):  
Ariyeh H. Maller ◽  
Ricardo A. Flores ◽  
Joel R. Primack
Keyword(s):  
Spiral Galaxy ◽  
Gravitational Lensing

scholarly journals Scale dependence of galaxy biasing investigated by weak gravitational lensing: An assessment using semi-analytic galaxies and simulated lensing data

2018 ◽  
Vol 613 ◽  
pp. A15 ◽  
Author(s):  
Patrick Simon ◽  
Stefan Hilbert
Keyword(s):  
Gravitational Lensing ◽  
Spatial Scales ◽  
Scale Dependence ◽  
Reconstruction Technique ◽  
Weak Gravitational Lensing ◽  
Physical Scale ◽  
The Galaxy ◽  
Galaxy Bias ◽  
The Impact ◽  
Lens Galaxies

Galaxies are biased tracers of the matter density on cosmological scales. For future tests of galaxy models, we refine and assess a method to measure galaxy biasing as a function of physical scalekwith weak gravitational lensing. This method enables us to reconstruct the galaxy bias factorb(k) as well as the galaxy-matter correlationr(k) on spatial scales between 0.01hMpc−1≲k≲ 10hMpc−1for redshift-binned lens galaxies below redshiftz≲ 0.6. In the refinement, we account for an intrinsic alignment of source ellipticities, and we correct for the magnification bias of the lens galaxies, relevant for the galaxy-galaxy lensing signal, to improve the accuracy of the reconstructedr(k). For simulated data, the reconstructions achieve an accuracy of 3–7% (68% confidence level) over the abovek-range for a survey area and a typical depth of contemporary ground-based surveys. Realistically the accuracy is, however, probably reduced to about 10–15%, mainly by systematic uncertainties in the assumed intrinsic source alignment, the fiducial cosmology, and the redshift distributions of lens and source galaxies (in that order). Furthermore, our reconstruction technique employs physical templates forb(k) andr(k) that elucidate the impact of central galaxies and the halo-occupation statistics of satellite galaxies on the scale-dependence of galaxy bias, which we discuss in the paper. In a first demonstration, we apply this method to previous measurements in the Garching-Bonn Deep Survey and give a physical interpretation of the lens population.

scholarly journals Gravitational lensing in 4-D Einstein–Gauss–Bonnet gravity in the presence of plasma

2021 ◽  
Vol 32 ◽  
pp. 100798
Author(s):  
Gulmina Zaman Babar ◽  
Farruh Atamurotov ◽  
Abdullah Zaman Babar
Keyword(s):  
Gravitational Lensing ◽  
Bonnet Gravity

scholarly journals Strong gravitational lensing by Kerr and Kerr-Newman black holes

2021 ◽  
Vol 103 (10) ◽  
Author(s):  
Tien Hsieh ◽  
Da-Shin Lee ◽  
Chi-Yong Lin
Keyword(s):  
Black Holes ◽  
Gravitational Lensing ◽  
Strong Gravitational Lensing

scholarly journals Quantifying the structure of strong gravitational lens potentials with uncertainty-aware deep neural networks

2020 ◽  
Vol 499 (4) ◽  
pp. 5641-5652
Author(s):  
Georgios Vernardos ◽  
Grigorios Tsagkatakis ◽  
Yannis Pantazis
Keyword(s):  
Confidence Intervals ◽  
Galaxy Evolution ◽  
Gravitational Lensing ◽  
Probability Distributions ◽  
Mass Density ◽  
Ground Truth ◽  
Gaussian Random Fields ◽  
Training Data ◽  
Gravitational Lens ◽  
Data Set

ABSTRACT Gravitational lensing is a powerful tool for constraining substructure in the mass distribution of galaxies, be it from the presence of dark matter sub-haloes or due to physical mechanisms affecting the baryons throughout galaxy evolution. Such substructure is hard to model and is either ignored by traditional, smooth modelling, approaches, or treated as well-localized massive perturbers. In this work, we propose a deep learning approach to quantify the statistical properties of such perturbations directly from images, where only the extended lensed source features within a mask are considered, without the need of any lens modelling. Our training data consist of mock lensed images assuming perturbing Gaussian Random Fields permeating the smooth overall lens potential, and, for the first time, using images of real galaxies as the lensed source. We employ a novel deep neural network that can handle arbitrary uncertainty intervals associated with the training data set labels as input, provides probability distributions as output, and adopts a composite loss function. The method succeeds not only in accurately estimating the actual parameter values, but also reduces the predicted confidence intervals by 10 per cent in an unsupervised manner, i.e. without having access to the actual ground truth values. Our results are invariant to the inherent degeneracy between mass perturbations in the lens and complex brightness profiles for the source. Hence, we can quantitatively and robustly quantify the smoothness of the mass density of thousands of lenses, including confidence intervals, and provide a consistent ranking for follow-up science.

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