1 code implementation • 15 Nov 2022 • Ji Won Park, Simon Birrer, Madison Ueland, Miles Cranmer, Adriano Agnello, Sebastian Wagner-Carena, Philip J. Marshall, Aaron Roodman, The LSST Dark Energy Science Collaboration
For each test set of 1, 000 sightlines, the BGNN infers the individual $\kappa$ posteriors, which we combine in a hierarchical Bayesian model to yield constraints on the hyperparameters governing the population.
no code implementations • 26 Aug 2020 • Pietro Bergamini, Adriano Agnello, Gabriel Caminha
We have devised a Bayesian hierarchical inference framework, which enables the determination of all lensing parameters and of the scaling-relation hyperparameters, including intrinsic scatter, from lensing constraints and (if given) stellar kinematic measurements.
Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies
1 code implementation • 17 Sep 2019 • Nikki Arendse, Radosław J. Wojtak, Adriano Agnello, Geoff C. -F. Chen, Christopher D. Fassnacht, Dominique Sluse, Stefan Hilbert, Martin Millon, Vivien Bonvin, Kenneth C. Wong, Frédéric Courbin, Sherry H. Suyu, Simon Birrer, Tommaso Treu, Leon V. E. Koopmans
Results from time-delay lenses are consistent with those from distance-ladder calibrations and point to a discrepancy between absolute distance scales measured from the CMB (assuming the standard cosmological model) and late-time observations.
Cosmology and Nongalactic Astrophysics
