Search Results for author:
Found 2 papers, 1 papers with code
A Bayesian Convolutional Neural Network for Robust Galaxy Ellipticity Regression
We show that while a convolutional network can be trained to correctly estimate well calibrated aleatoric uncertainty, -- the uncertainty due to the presence of noise in the images -- it is unable to generate a trustworthy ellipticity distribution when exposed to previously unseen data (i. e. here, blended scenes).
Deblending galaxies with Variational Autoencoders: a joint multi-band, multi-instrument approach
The apparent superposition of galaxies with other astrophysical objects along the line of sight, a problem known as blending, will be a major challenge for upcoming, ground-based, deep, photometric galaxy surveys, such as the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST).
Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics
