Photometric redshifts and quasar probabilities from a single, data-driven generative model

Journal Article · Tue Mar 20 00:00:00 EDT 2012 · Astrophysical Journal

DOI:https://doi.org/10.1088/0004-637X/749/1/41· OSTI ID:1053041

Bovy, Jo ^[1]; Myers, Adam D. ^[2]; Hennawi, Joseph F. ^[3]; Hogg, David W. ^[4]; McMahon, Richard G. ^[5]; Schiminovich, David ^[6]; Sheldon, Erin S. ^[7]; Brinkmann, Jon ^[8]; Schneider, Donald P. ^[9]; Weaver, Benjamin A. ^[1]

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1. New York Univ. (NYU), NY (United States)
2. Univ. of Wyoming, Laramie, WY (United States); Max Planck Inst. for Medical Research, Heidelberg (Germany)
3. Max Planck Inst. for Medical Research, Heidelberg (Germany)
4. Max Planck Inst. for Medical Research, Heidelberg (Germany); New York Univ. (NYU), NY (United States)
5. Univ. of Cambridge (United Kingdom)
6. Columbia Univ., New York, NY (United States)
7. Brookhaven National Lab. (BNL), Upton, NY (United States)
8. Apache Point Observatory and New Mexico State Univ., Sunspot, NM (United States)
9. Pennsylvania State Univ., University Park, PA (United States)

We describe a technique for simultaneously classifying and estimating the redshift of quasars. It can separate quasars from stars in arbitrary redshift ranges, estimate full posterior distribution functions for the redshift, and naturally incorporate flux uncertainties, missing data, and multi-wavelength photometry. We build models of quasars in flux-redshift space by applying the extreme deconvolution technique to estimate the underlying density. By integrating this density over redshift, one can obtain quasar flux densities in different redshift ranges. This approach allows for efficient, consistent, and fast classification and photometric redshift estimation. This is achieved by combining the speed obtained by choosing simple analytical forms as the basis of our density model with the flexibility of non-parametric models through the use of many simple components with many parameters. We show that this technique is competitive with the best photometric quasar classification techniques—which are limited to fixed, broad redshift ranges and high signal-to-noise ratio data—and with the best photometric redshift techniques when applied to broadband optical data. We demonstrate that the inclusion of UV and NIR data significantly improves photometric quasar-star separation and essentially resolves all of the redshift degeneracies for quasars inherent to the ugriz filter system, even when included data have a low signal-to-noise ratio. For quasars spectroscopically confirmed by the SDSS 84% and 97% of the objects with Galaxy Evolution Explorer UV and UKIDSS NIR data have photometric redshifts within 0.1 and 0.3, respectively, of the spectroscopic redshift; this amounts to about a factor of three improvement over ugriz-only photometric redshifts. Our code to calculate quasar probabilities and redshift probability distributions is publicly available.

• Research Organization: Brookhaven National Lab. (BNL), Upton, NY (United States)
• Sponsoring Organization: USDOE Office of Science (SC)
• DOE Contract Number: AC02-98CH10886
• OSTI ID: 1053041
• Report Number(s): BNL-98041-2012-JA; KA1301021
• Journal Information: Astrophysical Journal, Vol. 749, Issue 1; ISSN 0004-637X
• Publisher: Institute of Physics (IOP)
• Country of Publication: United States
• Language: English

Related Subjects

79 ASTRONOMY AND ASTROPHYSICS