Mathematics – Logic
Scientific paper
Jan 2010
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010aas...21540112s&link_type=abstract
American Astronomical Society, AAS Meeting #215, #401.12; Bulletin of the American Astronomical Society, Vol. 42, p.219
Mathematics
Logic
Scientific paper
LSST will provide a view of galaxies over a significant fraction of the age of the universe. In order to take full advantage of this vast dataset the LSST cosmological science cases require photometric redshifts to meet stringent design goals. In this poster we describe the expected photometric redshift performance of LSST based on empirical studies and simulations and describe ongoing work to characterize and minimize redshift uncertainties. We discuss the accuracy of photometric redshifts in relation to the specific filter system and our ability to photometrically calibrate the data. Bayesian priors provide a framework for incorporating additional knowledge, such as surface brightness, into the redshift determination. A well sampled training set of galaxy spectra forms the necessary basis for translating observables into redshifts. We discuss the training sets needed to span the full range of galaxy properties, as well as methods to deal with possible incompleteness; specifically, how the use of cross-correlation information enables measurement of accurate photometric redshift error distributions. This method can constrain redshift uncertainties for faint galaxies even with spectra of only a bright subsample.
Connolly Andrew J.
Ivezic Zeljko
LSST Collaboration
Matthews Daniel J.
Newman Jeffrey A.
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