25 citations · 30 across the 5 of their papers we have counts for
6 papers
Opportunities in AI/ML for the Rubin LSST Dark Energy Science Collaboration
LSST Dark Energy Science Collaboration, Eric Aubourg, Camille Avestruz +63
The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will produce unprecedented volumes of heterogeneous astronomical data (images, catalogs, and alerts) that cha…
GraViT: Transfer Learning with Vision Transformers and MLP-Mixer for Strong Gravitational Lens Discovery
René Parlange, Juan C. Cuevas-Tello, Octavio Valenzuela +4
Gravitational lensing offers a powerful probe into the properties of dark matter and is crucial to infer cosmological parameters. The Legacy Survey of Space and Time (LSST) is pred…
A Tutorial on Deep Neural Networks for Intelligent Systems
Juan C. Cuevas-Tello, Manuel Valenzuela-Rendon, Juan A. Nolazco-Flores
Developing Intelligent Systems involves artificial intelligence approaches including artificial neural networks. Here, we present a tutorial of Deep Neural Networks (DNNs), and som…
Kernel regression estimates of time delays between gravitationally lensed fluxes
Sultanah AL Otaibi, Peter Tiňo, Juan C Cuevas-Tello +2
Strongly lensed variable quasars can serve as precise cosmological probes, provided that time delays between the image fluxes can be accurately measured. A number of methods have b…
Uncovering delayed patterns in noisy and irregularly sampled time series: an astronomy application
Juan C. Cuevas-Tello, Peter Tino, Somak Raychaudhury +2
We study the problem of estimating the time delay between two signals representing delayed, irregularly sampled and noisy versions of the same underlying pattern. We propose and de…
How accurate are the time delay estimates in gravitational lensing?
Juan C. Cuevas-Tello, Peter Tino, Somak Raychaudhury
We present a novel approach to estimate the time delay between light curves of multiple images in a gravitationally lensed system, based on Kernel methods in the context of machine…