854 citations · 7.6k across the 93 of their papers we have counts for
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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…
Tuning target selection algorithms to improve galaxy redshift estimates
Ben Hoyle, Kerstin Paech, Markus Michael Rau +2
We showcase machine learning (ML) inspired target selection algorithms to determine which of all potential targets should be selected first for spectroscopic follow up. Efficient t…
Feature importance for machine learning redshifts applied to SDSS galaxies
Ben Hoyle, Markus Michael Rau, Roman Zitlau +2
We present an analysis of importance feature selection applied to photometric redshift estimation using the machine learning architecture Decision Trees with the ensemble learning…
The 64 Mpixel wide field imager for the Wendelstein 2m Telescope: Design and Calibration
Ralf Kosyra, Claus Goessl, Ulrich Hopp +4
The Wendelstein Observatory of Ludwig Maximilians University of Munich has recently been upgraded with a modern 2m robotic telescope. One Nasmyth port of the telescope has been equ…
Photometric redshift analysis in the Dark Energy Survey Science Verification data
C. Sánchez, M. Carrasco Kind, H. Lin +74
We present results from a study of the photometric redshift performance of the Dark Energy Survey (DES), using the early data from a Science Verification (SV) period of observation…