161 citations · 176 across the 5 of their papers we have counts for
5 papers
Improving Photometric Redshift Estimation for Cosmology with LSST using Bayesian Neural Networks
Evan Jones, Tuan Do, Bernie Boscoe +3
We present results exploring the role that probabilistic deep learning models can play in cosmology from large-scale astronomical surveys through photometric redshift (photo-z) est…
Elements of effective machine learning datasets in astronomy
Bernie Boscoe, Tuan Do, Evan Jones +3
In this work, we identify elements of effective machine learning datasets in astronomy and present suggestions for their design and creation. Machine learning has become an increas…
A surprising abundance of massive quiescent galaxies at 3 < z < 5 in the first data from JWST CEERS
A. C. Carnall, D. J. McLeod, R. J. McLure +9
We report a robust sample of 10 massive quiescent galaxies at redshift, , selected using the first data from the JWST CEERS programme. Three of these galaxies are at $4 < z…
Machine Learning Classification to Identify Catastrophic Outlier Photometric Redshift Estimates
J. Singal, G. Silverman, E. Jones +3
We present results of using a basic binary classification neural network model to identify likely catastrophic outlier photometric redshift estimates of individual galaxies, based…
Tests of Catastrophic Outlier Prediction in Empirical Photometric Redshift Estimation with Redshift Probability Distributions
Evan Jones, J. Singal
We present results of using individual galaxies' redshift probability information derived from a photometric redshift (photo-z) algorithm, SPIDERz, to identify potential catastroph…