MEASURING QUASAR VARIABILITY WITH Pan-STARRS1 AND SDSS

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 10 super(4) deg super(2) surveys. We use 10 super(5) spectroscopical...

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Veröffentlicht in:The Astrophysical journal 2014-04, Vol.784 (2), p.1-16
Hauptverfasser: Morganson, E, Burgett, W S, Chambers, K C, Green, P J, Kaiser, N, Magnier, E A, Marshall, P J, Morgan, J S, Price, P A, Rix, H-W, Schlafly, E F, Tonry, J L, Walter, F
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container_end_page 16
container_issue 2
container_start_page 1
container_title The Astrophysical journal
container_volume 784
creator Morganson, E
Burgett, W S
Chambers, K C
Green, P J
Kaiser, N
Magnier, E A
Marshall, P J
Morgan, J S
Price, P A
Rix, H-W
Schlafly, E F
Tonry, J L
Walter, F
description 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 10 super(4) deg super(2) surveys. We use 10 super(5) spectroscopically confirmed quasars that have been well measured in both PSI 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 [Delta] magnitudes, is consistent with V(z, L, t) = A sub(0)(1 + z) super(0.37)(L/L sub(0)) super(-0.16)(t/1 yr) super(0.246), where L sub(0) = 10 super(46) erg s super(-1) and A sub(0) = 0.190, 0.162, 0.147, or 0.141 in the g sub(P1), r sub(P1), i sub(P1), or z sub(P1) filter, respectively. We also fit across all four filters and obtain median variability as a function of z, L, and [lambda] as V(z, L, [lambda], t) = 0.079(1 + z) super(0.15)(L/L sub(0)) super(-0.2)([lambda]/1000nm) super(-0.44)(t /1 yr) super(0.246).
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We use 10 super(5) spectroscopically confirmed quasars that have been well measured in both PSI 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. 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subjects ASTROPHYSICS, COSMOLOGY AND ASTRONOMY
COLOR
Deltas
IMPURITIES
Photometry
Power law
QUASARS
RED SHIFT
Sky surveys (astronomy)
SPECTROSCOPY
TELESCOPES
title MEASURING QUASAR VARIABILITY WITH Pan-STARRS1 AND SDSS
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