Search Results for author:
Found 6 papers, 2 papers with code
Approximate Inference for Constructing Astronomical Catalogs from Images
We present a new, fully generative model for constructing astronomical catalogs from optical telescope image sets.
Cataloging the Visible Universe through Bayesian Inference at Petascale
We construct an astronomical catalog from 55 TB of imaging data using Celeste, a Bayesian variational inference code written entirely in the high-productivity programming language Julia.
Distributed, Parallel, and Cluster Computing Instrumentation and Methods for Astrophysics 85A35, 68W10, 62P35 J.2; D.1.3; G.3; I.2; D.2
Learning an Astronomical Catalog of the Visible Universe through Scalable Bayesian Inference
Celeste is a procedure for inferring astronomical catalogs that attains state-of-the-art scientific results.
A Gaussian Process Model of Quasar Spectral Energy Distributions
We propose a method for combining two sources of astronomical data, spectroscopy and photometry, that carry information about sources of light (e. g., stars, galaxies, and quasars) at extremely different spectral resolutions.
SDSS-III Baryon Oscillation Spectroscopic Survey Data Release 12: galaxy target selection and large scale structure catalogues
We also present the algorithms used to create large scale structure catalogues for the final Data Release (DR12) samples and the associated random catalogues that quantify the survey mask.
Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies
Celeste: Variational inference for a generative model of astronomical images
We present a new, fully generative model of optical telescope image sets, along with a variational procedure for inference.
