# Variability-Selected Quasars

## Contact

Nathalie Palanque-Delabrouille
Institute of Research into the Fundamental Laws of the Universe
CEA Saclay

## Summary

Candidate quasars in the 220-square-degree footprint of Stripe 82, selected by variability alone

## Finding Targets

An object whose ANCILLARY_TARGET2 value includes one or more of the bitmasks in the following table was targeted for spectroscopy as part of this ancillary target program. See SDSS bitmasks to learn how to use these values to identify objects in this ancillary target program.

Program (bit name) Bit number Target Description Number of Fibers Number of Unique Primary Objects
QSO_VAR_FPG 4 Candidate quasar in Stripe 82 survey area, selected from variability alone 639 609

## Description

Variability is used to improve the selection efficiency for quasars around z = 2.7 and z ~ 3.5, where they often lie in the region of color-color space that is occupied by the stellar locus. The selection method used in this Ancillary Target program is quite similar to the variability target selection used to select the main sample of quasars in Stripe 82, as described in BOSS Quasar Target Selection. The sample from this Ancillary Target program also complements the sample of the No Quasar Left Behind program, but the sample discussed here includes fainter quasars. This sample also results from a loose color selection, intended to select primarily objects at z > 2.15.

Data from this Ancillary Target program help to address the completeness of color selections, and help to identify obscured objects that would not otherwise be selected (more details are found in Palanque-Delabrouille et al. 2011). These data are also being used to demonstrate the efficiency of variability target selection for possible implementation in future surveys such as BigBOSS (Schlegel et al. 2011), a ground-based dark energy experiment to study baryon acoustic oscillations and the growth of structure with a wide-area galaxy and quasar redshift survey.

## Target Selection

In this method, objects were included with ifib2 > 18, (gPSF – iPSF) < 2.2, (uPSF – gPSF) > 0.4, and (c1 < 1.5 or c3 < 0). Colors c1 and c3 are defined in Fan et al. (1999) as:

c1 = 0.95(u – g) + 0.31(g – r) + 0.11(r – i) ,
c3 = -0.39(u – g) + 0.79(g – r) + 0.47(r – i) .

For each object, a variability neural network was used to quantify the likelihood that the object is a quasar. The neural network takes as input:

• the value of the χ2 fit between each object’s observed light curve in each band and a model which assumes no variability
• a structure function derived from the lightcurve as in Schmidt et al. (2010)

Targets require a probability of being a quasar from the variability neural network greater than 0.95. Candidate quasars are chosen from ~60 imaging epochs from the last ten years, resulting in about 15 quasar candidates per square degree. After removal of previously-known quasars, as well as candidates that had already been included in the main BOSS Quasar Target Selection, 3.4 objects per square degree were selected for this sample.

These data address the completeness of color selections and identifies obscured objects that would not be selected otherwise (Palanque-Delabrouille et al. 2011). These data are also being used to demonstrate the efficiency of variability target selection for possible implementation in future surveys such as BigBOSS (Schlegel et al. 2011), a ground-based dark energy experiment to study BAO and the growth of structure with deeper observations than BOSS.

## REFERENCES

Fan, X., et al. 1999, AJ, 118, 1, doi:10.1086/300944

Schlegel, D., et al. 2011, arXiv:1106.1706

Schmidt, K. B., et al. 2010, ApJ, 714, 1194, doi:10.1088/0004-637X/714/2/1194