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- W1491878901 abstract "I summarize our work on the construction of deep photometric quasar catalogs using a non‐parametric Bayesian classifier based on kernel density estimation. Using only 5‐band optical data from the Sloan Digital Sky Survey (SDSS), we have identified nearly 1,000,000 photometric quasars candidates over 0<z<5 in 8400 deg2. Overall the catalog is nearly 80% efficient (quasar candidates:true quasars), increasing to 97% for z<2.2 UVX objects. Using ∼30 deg2 of overlap between SDSS and Spitzer‐IRAC we extend our algorithm to 8‐D color selection and identify 5500 photometric quasars candidates, including up to 1200 type 2 (obscured) quasars. Our method has the potential for achieving quasar densities as high as 1000 deg−2 over large areas of sky. In addition to selection of quasars, we further determine photometric redshifts for our sample. From the SDSS 5‐band photometry, we are able to achieve accuracies as high at Δz±0.3 for ∼83% of the sample. Including MIR data from IRAC improves this fraction to ∼94%. The cl..." @default.
- W1491878901 created "2016-06-24" @default.
- W1491878901 creator A5039806724 @default.
- W1491878901 date "2008-01-01" @default.
- W1491878901 modified "2023-10-18" @default.
- W1491878901 title "Bayesian Quasar Selection and the Quasar Luminosity Function" @default.
- W1491878901 doi "https://doi.org/10.1063/1.3059053" @default.
- W1491878901 hasPublicationYear "2008" @default.
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