22 citations · 68 across the 12 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…
Galaxy Spectra neural Network (GaSNet). II. Using Deep Learning for Spectral Classification and Redshift Predictions
Fucheng Zhong, Nicola R. Napolitano, Caroline Heneka +25
Large sky spectroscopic surveys have reached the scale of photometric surveys in terms of sample sizes and data complexity. These huge datasets require efficient, accurate, and fle…
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…