1.2k citations · 5.1k across the 72 of their papers we have counts for
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astro-ph.CO2016★ 9 cited
Correcting cosmological parameter biases for all redshift surveys induced by estimating and reweighting redshift distributions
Markus Michael Rau, Ben Hoyle, Kerstin Paech +1
Photometric redshift uncertainties are a major source of systematic error for ongoing and future photometric surveys. We study different sources of redshift error caused by choosin…
astro-ph.IM2016★ 27 cited
Stacking for machine learning redshifts applied to SDSS galaxies
Roman Zitlau, Ben Hoyle, Kerstin Paech +3
We present an analysis of a general machine learning technique called 'stacking' for the estimation of photometric redshifts. Stacking techniques can feed the photometric redshift…