From the 1 of 8 linked papers with an AI index.
2 citations · 2 across the 2 of their papers we have counts for
8 papers
Investigating the Dark Energy Constraint from Strongly Lensed AGN at LSST-Scale
Sydney Erickson, Martin Millon, Padmavathi Venkatraman +12
The paper presents a scalable hierarchical inference framework to jointly analyze hundreds of strongly lensed AGN time delays from LSST, forecasting a ~2.5% measurement of H0 and a…
Strong Lensing Tomography: Double and pseudo multi-source plane strong gravitational lensing to constrain dark energy
Paras Sharma, Simon Birrer, Narayan Khadka +12
Tomographic measurements of gravitational lensing with different lens and source redshift distributions contain crucial information about the universe's relative expansion rate, an…
SLSim: a strong lensing population simulation package
Narayan Khadka, Simon Birrer, Henry Best +45
Gravitational lensing offers unique insights into cosmology by bending light around massive objects. Strong gravitational lensing, in particular, produces magnified and often multi…
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…
Lens Model Accuracy in the Expected LSST Lensed AGN Sample
Padmavathi Venkatraman, Sydney Erickson, Phil Marshall +13
Strong gravitational lensing of active galactic nuclei (AGN) enables measurements of cosmological parameters through time-delay cosmography (TDC). With data from the upcoming LSST…
Lens Modeling of STRIDES Strongly Lensed Quasars using Neural Posterior Estimation
Sydney Erickson, Sebastian Wagner-Carena, Phil Marshall +9
Strongly lensed quasars can be used to constrain cosmological parameters through time-delay cosmography. Models of the lens masses are a necessary component of this analysis. To en…