TY - JOUR
T1 - Measuring quasar variability with pan-STARRS1 and SDSS
AU - Morganson, E.
AU - Burgett, W. S.
AU - Chambers, K. C.
AU - Green, P. J.
AU - Kaiser, N.
AU - Magnier, E. A.
AU - Marshall, P. J.
AU - Morgan, J. S.
AU - Price, P. A.
AU - Rix, H. W.
AU - Schlafly, E. F.
AU - Tonry, J. L.
AU - Walter, F.
PY - 2014/4/1
Y1 - 2014/4/1
N2 - We measure quasar variability using the Panoramic Survey Telescope and Rapid Response System 1 Survey (Pan-STARRS1 or PS1) and the Sloan Digital Sky Survey (SDSS) and establish a method of selecting quasars via their variability in 104 deg2 surveys. We use 105 spectroscopically confirmed quasars that have been well measured in both PS1 and SDSS and take advantage of the decadal timescales that separate SDSS measurements and PS1 measurements. A power law model fits the data well over the entire time range tested, 0.01-10 yr. Variability in the current PS1-SDSS data set can efficiently distinguish between quasars and nonvarying objects. It improves the purity of a griz quasar color cut from 4.1% to 48% while maintaining 67% completeness. Variability will be very effective at finding quasars in data sets with no u band and in redshift ranges where exclusively photometric selection is not efficient. We show that quasars' rest-frame ensemble variability, measured as a root mean squared in Δ magnitudes, is consistent with V(z, L, t) = A 0(1 + z)0.37(L/L 0) -0.16(t/1 yr)0.246, where L 0 = 1046 erg s-1 and A 0 = 0.190, 0.162, 0.147, or 0.141 in the g P1, r P1, i P1, or z P1filter, respectively. We also fit across all four filters and obtain median variability as a function of z, L, and λ as V(z, L, λ, t) = 0.079(1 + z)0.15(L/L 0) -0.2(λ/1000 nm)-0.44(t/1 yr)0.246.
AB - We measure quasar variability using the Panoramic Survey Telescope and Rapid Response System 1 Survey (Pan-STARRS1 or PS1) and the Sloan Digital Sky Survey (SDSS) and establish a method of selecting quasars via their variability in 104 deg2 surveys. We use 105 spectroscopically confirmed quasars that have been well measured in both PS1 and SDSS and take advantage of the decadal timescales that separate SDSS measurements and PS1 measurements. A power law model fits the data well over the entire time range tested, 0.01-10 yr. Variability in the current PS1-SDSS data set can efficiently distinguish between quasars and nonvarying objects. It improves the purity of a griz quasar color cut from 4.1% to 48% while maintaining 67% completeness. Variability will be very effective at finding quasars in data sets with no u band and in redshift ranges where exclusively photometric selection is not efficient. We show that quasars' rest-frame ensemble variability, measured as a root mean squared in Δ magnitudes, is consistent with V(z, L, t) = A 0(1 + z)0.37(L/L 0) -0.16(t/1 yr)0.246, where L 0 = 1046 erg s-1 and A 0 = 0.190, 0.162, 0.147, or 0.141 in the g P1, r P1, i P1, or z P1filter, respectively. We also fit across all four filters and obtain median variability as a function of z, L, and λ as V(z, L, λ, t) = 0.079(1 + z)0.15(L/L 0) -0.2(λ/1000 nm)-0.44(t/1 yr)0.246.
KW - quasars: general
UR - http://www.scopus.com/inward/record.url?scp=84896508390&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84896508390&partnerID=8YFLogxK
U2 - 10.1088/0004-637X/784/2/92
DO - 10.1088/0004-637X/784/2/92
M3 - Article
AN - SCOPUS:84896508390
SN - 0004-637X
VL - 784
JO - Astrophysical Journal
JF - Astrophysical Journal
IS - 2
M1 - 92
ER -