Astronomy and Astrophysics – Astronomy
Scientific paper
Jan 2011
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2011aas...21715004s&link_type=abstract
American Astronomical Society, AAS Meeting #217, #150.04; Bulletin of the American Astronomical Society, Vol. 43, 2011
Astronomy and Astrophysics
Astronomy
1
Scientific paper
Future large-scale optical surveys, such as the Dark Energy Survey, will require photometric redshift estimates for hundreds of millions of galaxies. In general, the magnitude and color distributions of these galaxies are expected to differ from those of the spectroscopic sets used for training empirically-based photometric redshift algorithms. In this analysis, we investigate the robustness of the ArborZ boosted decision tree method for estimating photometric redshifts in these non-representative cases. We show that ArborZ performs well in these cases, provided that the training and target sets have sufficient overlap in parameter space. We also compare the results of training ArborZ on magnitudes, colors, and a combination of the two.
Gerdes David W.
Sypniewski Adam J.
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