550 citations · 650 across the 17 of their papers we have counts for
3 papers · 1 filter
Identifying type II quasars at intermediate redshift with few-shot learning photometric classification
P. A. C. Cunha, A. Humphrey, J. Brinchmann +5
We aim to identify QSO2 candidates in the redshift desert using optical and infrared photometry. At this intermediate redshift range, most of the prominent optical emission lines i…
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…