no code implementations • 5 Jan 2019 • Warren R. Morningstar, Laurence Perreault Levasseur, Yashar D. Hezaveh, Roger Blandford, Phil Marshall, Patrick Putzky, Thomas D. Rueter, Risa Wechsler, Max Welling
We present a machine learning method for the reconstruction of the undistorted images of background sources in strongly lensed systems.
Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies
no code implementations • 31 Jul 2018 • Warren R. Morningstar, Yashar D. Hezaveh, Laurence Perreault Levasseur, Roger D. Blandford, Philip J. Marshall, Patrick Putzky, Risa H. Wechsler
We use convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to estimate the parameters of strong gravitational lenses from interferometric observations.
Instrumentation and Methods for Astrophysics
no code implementations • 29 Aug 2017 • Laurence Perreault Levasseur, Yashar D. Hezaveh, Risa H. Wechsler
In Hezaveh et al. 2017 we showed that deep learning can be used for model parameter estimation and trained convolutional neural networks to determine the parameters of strong gravitational lensing systems.
Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics
1 code implementation • 29 Aug 2017 • Yashar D. Hezaveh, Laurence Perreault Levasseur, Philip J. Marshall
Quantifying image distortions caused by strong gravitational lensing and estimating the corresponding matter distribution in lensing galaxies has been primarily performed by maximum likelihood modeling of observations.
Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics
