S
Sebastian Wagner-Carena
Researcher at Stanford University
Publications - 11
Citations - 475
Sebastian Wagner-Carena is an academic researcher from Stanford University. The author has contributed to research in topics: Gravitational lens & Dark matter. The author has an hindex of 6, co-authored 8 publications receiving 172 citations. Previous affiliations of Sebastian Wagner-Carena include SLAC National Accelerator Laboratory.
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Journal ArticleDOI
TDCOSMO IV: Hierarchical time-delay cosmography -- joint inference of the Hubble constant and galaxy density profiles
Simon Birrer,Anowar J. Shajib,A. Galan,M. Millon,Tommaso Treu,Adriano Agnello,Matthew W. Auger,Geoff C. F. Chen,Lise Christensen,Thomas E. Collett,Frederic Courbin,Christopher D. Fassnacht,Christopher D. Fassnacht,Léon V. E. Koopmans,Philip J. Marshall,Ji Won Park,Cristian E. Rusu,Dominique Sluse,Chiara Spiniello,Chiara Spiniello,Sherry H. Suyu,Sherry H. Suyu,Sherry H. Suyu,Sebastian Wagner-Carena,Kenneth C. Wong,Matteo Barnabè,Adam S. Bolton,Oliver Czoske,Xuheng Ding,Joshua A. Frieman,Joshua A. Frieman,L. Van de Vyvere +31 more
TL;DR: In this article, a hierarchical Bayesian approach was proposed to estimate the mass-sheet transform (MST) with respect to stellar kinematics, which is based on a family of mass models.
Journal ArticleDOI
TDCOSMO IV: Hierarchical time-delay cosmography -- joint inference of the Hubble constant and galaxy density profiles
Simon Birrer,Anowar J. Shajib,A. Galan,M. Millon,Tommaso Treu,Adriano Agnello,Matthew W. Auger,Geoff C. F. Chen,Lise Christensen,Thomas E. Collett,Frederic Courbin,Christopher D. Fassnacht,Christopher D. Fassnacht,Léon V. E. Koopmans,Philip J. Marshall,Ji Won Park,Cristian E. Rusu,Dominique Sluse,Chiara Spiniello,Chiara Spiniello,Sherry H. Suyu,Sherry H. Suyu,Sherry H. Suyu,Sebastian Wagner-Carena,Kenneth C. Wong,Matteo Barnabè,Adam S. Bolton,Oliver Czoske,Xuheng Ding,Joshua A. Frieman,Joshua A. Frieman,L. Van de Vyvere +31 more
TL;DR: In this paper, a hierarchical approach was proposed to estimate the mass-sheet transform (MST) with respect to the stellar anisotropic kinematics of the lensing observables, which was validated on hydrodynamically simulated lenses.
Journal ArticleDOI
lenstronomy II: A gravitational lensing software ecosystem
Simon Birrer,Anowar J. Shajib,Daniel Gilman,A. Galan,Jelle Aalbers,M. Millon,Robert Morgan,Giulia Pagano,Ji Won Park,Luca Teodori,Nicolas Tessore,Madison Ueland,Lyne Van de Vyvere,Sebastian Wagner-Carena,Ewoud Wempe,Lilan Yang,Xuheng Ding,Thomas Schmidt,Dominique Sluse,Ming Zhang,Adam Amara +20 more
TL;DR: lenstronomy as discussed by the authors is an Astropy-affiliated Python package for gravitational lensing simulations and analyses, which has become an integral part of a wide range of recent analyses, such as measuring the Hubble constant with time-delay strong lensing.
Journal ArticleDOI
lenstronomy II: A gravitational lensing software ecosystem
Simon Birrer,Anowar J. Shajib,Daniel Gilman,A. Galan,Jelle Aalbers,M. Millon,Robert Morgan,Giulia Pagano,Ji Won Park,Luca Teodori,Nicolas Tessore,Madison Ueland,Lyne Van de Vyvere,Sebastian Wagner-Carena,Ewoud Wempe,Lilan Yang,Xuheng Ding,Thomas Schmidt,Dominique Sluse,Ming Zhang,Adam Amara +20 more
TL;DR: Through community engagement and involvement, lenstronomy has become a foundation of an ecosystem of affiliated packages extending the original scope of the software and proving its robustness and applicability at the forefront of the strong gravitational lensing community in an open source and reproducible manner.
Journal ArticleDOI
Hierarchical Inference with Bayesian Neural Networks: An Application to Strong Gravitational Lensing
Sebastian Wagner-Carena,Sebastian Wagner-Carena,Ji Won Park,Ji Won Park,Simon Birrer,Simon Birrer,Philip J. Marshall,Philip J. Marshall,A. Roodman,A. Roodman,Risa H. Wechsler,Risa H. Wechsler +11 more
TL;DR: This work incorporates BNNs with flexible posterior parameterizations into a hierarchical inference framework that allows for the reconstruction of population hyperparameters and removes the bias introduced by the training distribution.