Photometric Redshifts Using Boosted Decision Trees

Details Photometric Redshifts Using Boosted Decision Trees Photometric Redshifts Using Boosted Decision Trees

Jan 2009

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2009aas...21348303g&link_type=abstract

American Astronomical Society, AAS Meeting #213, #483.03; Bulletin of the American Astronomical Society, Vol. 41, p.452

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Precision photometric redshifts will be essential for extracting cosmological parameters from the next generation of wide-area surveys. I describe a new photometric redshift algorithm based on the machine-learning technique of Boosted Decision Trees (BDTs). The performance of the algorithm is evaluated using data from the Sloan Digital Sky Survey as well as a mock catalog intended to simulate the sensitivity of the upcoming Dark Energy Survey, and compared to the performance of existing photometric redshift estimators. I describe how redshift errors and the fraction of catastrophic failures are estimated. I also show that the inclusion of shape information along with the photometric data significantly improves the redshift determination of highly-inclined spiral galaxies.

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