Risa H. Wechsler

TL;DR: In this paper, the sky localization of the first observed compact binary merger is presented, where the authors describe the low-latency analysis of the LIGO data and present a sky localization map.

TL;DR: In this paper, the authors used convolutional neural networks and recurrent neural networks (RNNs) to estimate the parameters of strong gravitational lenses from interferometric observations, and found that the best results are obtained when the effects of the dirty beam are first removed from the images with deconvolution performed with an RNN-based structure before estimating the parameters.

TL;DR: In this paper, a machine learning method for the reconstruction of the undistorted images of background sources in strongly lensed systems is presented, which treats the source as a pixelated image and utilizes the Recurrent Inference Machine (RIM) to iteratively reconstruct the background source given a lens model.

TL;DR: In this paper, the authors investigate unbound dark matter particles in halos by tracing particle trajectories in a simulation run to the far future (a = 100) and find that the traditional sum of kinetic and potential energies is a very poor predictor of which dark matter particle will eventually become unbound from halos.

TL;DR: In this paper, the authors extracted halos along past lightcones from the Bolshoi Planck dissipationless N-body simulations and populate these halos with galaxies using two different independently developed semi-analytic models of galaxy formation and the empirical model UniverseMachine.