550 citations · 650 across the 17 of their papers we have counts for
3 papers · 1 filter
Improving machine learning-derived photometric redshifts and physical property estimates using unlabelled observations
A. Humphrey, P. A. C. Cunha, A. Paulino-Afonso +5
In the era of huge astronomical surveys, machine learning offers promising solutions for the efficient estimation of galaxy properties. The traditional, `supervised' paradigm for t…
Machine-learning classification of astronomical sources: estimating F1-score in the absence of ground truth
A. Humphrey, W. Kuberski, J. Bialek +5
Machine-learning based classifiers have become indispensable in the field of astrophysics, allowing separation of astronomical sources into various classes, with computational effi…
Euclid preparation: XXII. Selection of Quiescent Galaxies from Mock Photometry using Machine Learning
Euclid Collaboration, A. Humphrey, L. Bisigello +206
The Euclid Space Telescope will provide deep imaging at optical and near-infrared wavelengths, along with slitless near-infrared spectroscopy, across ~15,000 sq deg of the sky. Euc…