Search Results for author: Sebastian Wagner-Carena

Found 5 papers, 3 papers with code

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

1 code implementation15 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.

Large-Scale Gravitational Lens Modeling with Bayesian Neural Networks for Accurate and Precise Inference of the Hubble Constant

2 code implementations30 Nov 2020 Ji Won Park, Sebastian Wagner-Carena, Simon Birrer, Philip J. Marshall, Joshua Yao-Yu Lin, Aaron Roodman

The computation time for the entire pipeline -- including the training set generation, BNN training, and $H_0$ inference -- translates to 9 minutes per lens on average for 200 lenses and converges to 6 minutes per lens as the sample size is increased.

Hierarchical Inference With Bayesian Neural Networks: An Application to Strong Gravitational Lensing

1 code implementation26 Oct 2020 Sebastian Wagner-Carena, Ji Won Park, Simon Birrer, Philip J. Marshall, Aaron Roodman, Risa H. Wechsler

We show that the posterior PDFs are sufficiently accurate (i. e., statistically consistent with the truth) across a wide variety of power-law elliptical lens mass distributions.

A Novel CMB Component Separation Method: Hierarchical Generalized Morphological Component Analysis

no code implementations17 Oct 2019 Sebastian Wagner-Carena, Max Hopkins, Ana Diaz Rivero, Cora Dvorkin

We present a novel technique for Cosmic Microwave Background (CMB) foreground subtraction based on the framework of blind source separation.

Simulated Annealing for JPEG Quantization

no code implementations3 Sep 2017 Max Hopkins, Michael Mitzenmacher, Sebastian Wagner-Carena

JPEG is one of the most widely used image formats, but in some ways remains surprisingly unoptimized, perhaps because some natural optimizations would go outside the standard that defines JPEG.


Cannot find the paper you are looking for? You can Submit a new open access paper.