%0 Generic %1 peters2015quasar %A Peters, Christina M. %A Richards, Gordon T. %A Myers, Adam D. %A Strauss, Michael A. %A Schmidt, Kasper B. %A Ivezić, Željko %A Ross, Nicholas P. %A MacLeod, Chelsea L. %A Riegel, Ryan %D 2015 %K classification color quasar variability %T Quasar Classification Using Color and Variability %U http://arxiv.org/abs/1508.04121 %X We conduct a pilot investigation to determine the optimal combination of color and variability information to identify quasars in current and future multi-epoch optical surveys. We use a Bayesian quasar selection algorithm (Richards et al. 2004) to identify 35,820 type 1 quasar candidates in a 239 square degree field of the Sloan Digital Sky Survey (SDSS) Stripe 82, using a combination of optical photometry and variability. Color analysis is performed on 5-band single- and multi-epoch SDSS optical photometry to a depth of r ~22.4. From these data, variability parameters are calculated by fitting the structure function of each object in each band with a power law model using 10 to >100 observations over timescales from ~1 day to ~8 years. Selection was based on a training sample of 13,221 spectroscopically-confirmed type-1 quasars, largely from the SDSS. Using variability alone, colors alone, and combining variability and colors we achieve 91%, 93%, and 97% quasar completeness and 98%, 98%, and 97% efficiency respectively, with particular improvement in the selection of quasars at 2.7<z<3.5 where quasars and stars have similar optical colors. The 22,867 quasar candidates that are not spectroscopically confirmed reach a depth of i ~22.0; 21,876 (95.7%) are dimmer than coadded i-band magnitude of 19.9, the cut off for spectroscopic follow-up for SDSS on Stripe 82. Brighter than 19.9, we find 5.7% more quasar candidates without confirming spectra in sky regions otherwise considered complete. The resulting quasar sample has sufficient purity (and statistically correctable incompleteness) to produce a luminosity function comparable to those determined by spectroscopic investigations. We discuss improvements that can be made to the process in preparation for performing similar photometric selection and science on data from post-SDSS sky surveys.

@misc{peters2015quasar, abstract = {We conduct a pilot investigation to determine the optimal combination of color and variability information to identify quasars in current and future multi-epoch optical surveys. We use a Bayesian quasar selection algorithm (Richards et al. 2004) to identify 35,820 type 1 quasar candidates in a 239 square degree field of the Sloan Digital Sky Survey (SDSS) Stripe 82, using a combination of optical photometry and variability. Color analysis is performed on 5-band single- and multi-epoch SDSS optical photometry to a depth of r ~22.4. From these data, variability parameters are calculated by fitting the structure function of each object in each band with a power law model using 10 to >100 observations over timescales from ~1 day to ~8 years. Selection was based on a training sample of 13,221 spectroscopically-confirmed type-1 quasars, largely from the SDSS. Using variability alone, colors alone, and combining variability and colors we achieve 91%, 93%, and 97% quasar completeness and 98%, 98%, and 97% efficiency respectively, with particular improvement in the selection of quasars at 2.7<z<3.5 where quasars and stars have similar optical colors. The 22,867 quasar candidates that are not spectroscopically confirmed reach a depth of i ~22.0; 21,876 (95.7%) are dimmer than coadded i-band magnitude of 19.9, the cut off for spectroscopic follow-up for SDSS on Stripe 82. Brighter than 19.9, we find 5.7% more quasar candidates without confirming spectra in sky regions otherwise considered complete. The resulting quasar sample has sufficient purity (and statistically correctable incompleteness) to produce a luminosity function comparable to those determined by spectroscopic investigations. We discuss improvements that can be made to the process in preparation for performing similar photometric selection and science on data from post-SDSS sky surveys.}, added-at = {2015-08-18T10:13:45.000+0200}, author = {Peters, Christina M. and Richards, Gordon T. and Myers, Adam D. and Strauss, Michael A. and Schmidt, Kasper B. and Ivezić, Željko and Ross, Nicholas P. and MacLeod, Chelsea L. and Riegel, Ryan}, biburl = {https://www.bibsonomy.org/bibtex/2bb742403d752b3b6749a3102d966362f/miki}, description = {[1508.04121] Quasar Classification Using Color and Variability}, interhash = {c023169db12e3a9438ae2d3c0280894c}, intrahash = {bb742403d752b3b6749a3102d966362f}, keywords = {classification color quasar variability}, note = {cite arxiv:1508.04121Comment: 32 pages, 23 figures. Accepted for publication in ApJS. Data file is available at http://oberon.physics.drexel.edu/~tinapeters/quasarclassification/Peters2015Catalog_30042015.fit.bz2}, timestamp = {2015-08-18T10:13:45.000+0200}, title = {Quasar Classification Using Color and Variability}, url = {http://arxiv.org/abs/1508.04121}, year = 2015 }