Bayesian photometric redshifts with empirical training sets
We combine in a single framework the two complementary benefits of χ2 template fits and empirical training sets used e.g. in neural nets: χ2 is more reliable when its probability density functions (PDFs) are inspected for multiple peaks, while empirical
|Collections||ANU Research Publications|
|Source:||Monthly Notices of the Royal Astronomical Society|
|01_Wolf_Bayesian_photometric_redshifts_2009.pdf||2.94 MB||Adobe PDF||Request a copy|
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