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Modeling strong gravitational lenses in order to quantify the distortions in the images of background sources and to reconstruct the mass density in the foreground lenses has traditionally been a difficult computational challenge. As the…

天体物理仪器与方法 · 物理学 2022-07-05 Alexandre Adam , Laurence Perreault-Levasseur , Yashar Hezaveh

We present a machine learning method for the reconstruction of the undistorted images of background sources in strongly lensed systems. This method treats the source as a pixelated image and utilizes the Recurrent Inference Machine (RIM) to…

We describe a new method for analyzing gravitational lens images, for the case where the source light distribution is pixelized. The method is suitable for high resolution, high S/N data of a multiply-imaged extended source. For a given…

天体物理学 · 物理学 2009-11-07 Steve Warren , Simon Dye

Strong gravitational lensing is a powerful technique for probing galaxy mass distributions and for measuring cosmological parameters. We present a pixelated approach to modeling simultaneously the lens potential and source intensity of…

天体物理学 · 物理学 2010-04-29 S. H. Suyu , P. J. Marshall , R. D. Blandford , C. D. Fassnacht , L. V. E. Koopmans , J. P. McKean , T. Treu

Strong-lensing images provide a wealth of information both about the magnified source and about the dark matter distribution in the lens. Precision analyses of these images can be used to constrain the nature of dark matter. However, this…

天体物理仪器与方法 · 物理学 2022-05-25 Konstantin Karchev , Adam Coogan , Christoph Weniger

From the nature of dark matter to the rate of expansion of our Universe, observations of distant galaxies distorted through strong gravitational lensing have the potential to answer some of the major open questions in astrophysics. Modeling…

宇宙学与河外天体物理 · 物理学 2022-10-18 Siddharth Mishra-Sharma , Ge Yang

Gravitational lensing is a powerful tool for constraining substructure in the mass distribution of galaxies, be it from the presence of dark matter sub-halos or due to physical mechanisms affecting the baryons throughout galaxy evolution.…

星系天体物理 · 物理学 2020-10-28 Georgios Vernardos , Grigorios Tsagkatakis , Yannis Pantazis

The analysis of optical images of galaxy-galaxy strong gravitational lensing systems can provide important information about the distribution of dark matter at small scales. However, the modeling and statistical analysis of these images is…

宇宙学与河外天体物理 · 物理学 2020-11-30 Adam Coogan , Konstantin Karchev , Christoph Weniger

We propose RelitLRM, a Large Reconstruction Model (LRM) for generating high-quality Gaussian splatting representations of 3D objects under novel illuminations from sparse (4-8) posed images captured under unknown static lighting. Unlike…

计算机视觉与模式识别 · 计算机科学 2024-10-11 Tianyuan Zhang , Zhengfei Kuang , Haian Jin , Zexiang Xu , Sai Bi , Hao Tan , He Zhang , Yiwei Hu , Milos Hasan , William T. Freeman , Kai Zhang , Fujun Luan

We present a non-parametric technique to infer the projected-mass distribution of a gravitational lens system with multiple strong-lensed images. The technique involves a dynamic grid in the lens plane on which the mass distribution of the…

天体物理学 · 物理学 2009-11-11 J. Liesenborgs , S. De Rijcke , H. Dejonghe

Gravitational lens modeling of spatially resolved sources is a challenging inverse problem with many observational constraints and model parameters. We examine established pixel-based source reconstruction algorithms for de-lensing the…

宇宙学与河外天体物理 · 物理学 2015-06-22 Amitpal Tagore , Charles Keeton

Recovering high-resolution images from limited sensory data typically leads to a serious ill-posed inverse problem, demanding inversion algorithms that effectively capture the prior information. Learning a good inverse mapping from training…

计算机视觉与模式识别 · 计算机科学 2018-06-12 Morteza Mardani , Qingyun Sun , Shreyas Vasawanala , Vardan Papyan , Hatef Monajemi , John Pauly , David Donoho

We revisit the issue of non-parametric gravitational lens reconstruction and present a new method to obtain the cluster mass distribution using strong lensing data without using any prior information on the underlying mass. The method…

天体物理学 · 物理学 2009-10-07 J. M. Diego , P. Protopapas , H. B Sandvik , M. Tegmark

Galaxies acting as gravitational lenses are surrounded by, at most, a handful of images. This apparent paucity of information forces one to make the best possible use of what information is available to invert the lens system. In this…

天体物理学 · 物理学 2009-11-13 J. Liesenborgs , S. De Rijcke , H. Dejonghe , P. Bekaert

In the coming years, strong gravitational lens discoveries are expected to increase in frequency by two orders of magnitude. Lens-modelling techniques are being developed to prepare for the coming massive influx of new lens data, and blind…

星系天体物理 · 物理学 2020-06-18 Philipp Denzel , Sampath Mukherjee , Jonathan P. Coles , Prasenjit Saha

If an extended source, such as a galaxy, is gravitationally lensed by a massive object in the foreground, the lensing distorts the observed image. It is straightforward to simulate what the observed image would be for a particular lens and…

天体物理学 · 物理学 2009-11-13 Brendon J. Brewer , Geraint F. Lewis

We investigate the detection prospects for gravitational lensing of three-dimensional maps from upcoming line intensity surveys, focusing in particular on the impact of gravitational nonlinearities on standard quadratic lensing estimators.…

宇宙学与河外天体物理 · 物理学 2018-09-05 Simon Foreman , P. Daniel Meerburg , Alexander van Engelen , Joel Meyers

The maximum-entropy method is applied to the problem of reconstructing the projected mass density of a galaxy cluster using its gravitational lensing effects on background galaxies. We demonstrate the method by reconstructing the mass…

天体物理学 · 物理学 2009-10-30 S. L. Bridle , M. P. Hobson , A. N. Lasenby , Richard Saunders

Gravitational lensing has become one of the most powerful tools available for investigating the 'dark side' of the universe. Cosmological strong gravitational lensing, in particular, probes the properties of the dense cores of dark matter…

宇宙学与河外天体物理 · 物理学 2016-09-30 Nan Li , Michael D. Gladders , Esteban M. Rangel , Michael K. Florian , Lindsey E. Bleem , Katrin Heitmann , Salman Habib , Patricia Fasel
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