Hierarchical Inference of the Lensing Convergence from Photometric Catalogs with Bayesian Graph Neural Networks

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.

A Modular Deep Learning Pipeline for Galaxy-Scale Strong Gravitational Lens Detection and Modeling

no code implementations • 10 Nov 2019 • Sandeep Madireddy, Nesar Ramachandra, Nan Li, James Butler, Prasanna Balaprakash, Salman Habib, Katrin Heitmann, The LSST Dark Energy Science Collaboration

Upcoming large astronomical surveys are expected to capture an unprecedented number of strong gravitational lensing systems.

Denoising Image Generation

The Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC): Data set

3 code implementations • 28 Sep 2018 • The PLAsTiCC team, Tarek Allam Jr., Anita Bahmanyar, Rahul Biswas, Mi Dai, Lluís Galbany, Renée Hložek, Emille E. O. Ishida, Saurabh W. Jha, David O. Jones, Richard Kessler, Michelle Lochner, Ashish A. Mahabal, Alex I. Malz, Kaisey S. Mandel, Juan Rafael Martínez-Galarza, Jason D. McEwen, Daniel Muthukrishna, Gautham Narayan, Hiranya Peiris, Christina M. Peters, Kara Ponder, Christian N. Setzer, The LSST Dark Energy Science Collaboration, The LSST Transients, Variable Stars Science Collaboration

The Photometric LSST Astronomical Time Series Classification Challenge (PLAsTiCC) is an open data challenge to classify simulated astronomical time-series data in preparation for observations from the Large Synoptic Survey Telescope (LSST), which will achieve first light in 2019 and commence its 10-year main survey in 2022.

Instrumentation and Methods for Astrophysics Solar and Stellar Astrophysics