### Abstract

We present a novel method for the optimal selection of quasars using time-series observations in a single photometric bandpass. Utilizing the damped random walk model of Kelly etal., we parameterize the ensemble quasar structure function in Sloan Stripe 82 as a function of observed brightness. The ensemble model fit can then be evaluated rigorously for and calibrated with individual light curves with no parameter fitting. This yields a classification in two statistics - one describing the fit confidence and the other describing the probability of a false alarm - which can be tuned, a priori, to achieve high quasar detection fractions (99% completeness with default cuts), given an acceptable rate of false alarms. We establish the typical rate of false alarms due to known variable stars as ≲3% (high purity). Applying the classification, we increase the sample of potential quasars relative to those known in Stripe 82 by as much as 29%, and by nearly a factor of two in the redshift range 2.5 < z < 3, where selection by color is extremely inefficient. This represents 1875 new quasars in a 290 deg^{2} field. The observed rates of both quasars and stars agree well with the model predictions, with >99% of quasars exhibiting the expected variability profile. We discuss the utility of the method at high redshift and in the regime of noisy and sparse data. Our time-series selection complements well-independent selection based on quasar colors and has strong potential for identifying high-redshift quasars for Baryon Acoustic Oscillations and other cosmology studies in the LSST era.

Original language | English (US) |
---|---|

Article number | 93 |

Journal | Astronomical Journal |

Volume | 141 |

Issue number | 3 |

DOIs | |

State | Published - Mar 2011 |

Externally published | Yes |

### Fingerprint

### Keywords

- cosmology: miscellaneous
- methods: statistical
- quasars: general
- stars: variables: general

### ASJC Scopus subject areas

- Space and Planetary Science
- Astronomy and Astrophysics

### Cite this

*Astronomical Journal*,

*141*(3), [93]. https://doi.org/10.1088/0004-6256/141/3/93

**Optimal time-series selection of quasars.** / Butler, Nathaniel; Bloom, Joshua S.

Research output: Contribution to journal › Article

*Astronomical Journal*, vol. 141, no. 3, 93. https://doi.org/10.1088/0004-6256/141/3/93

}

TY - JOUR

T1 - Optimal time-series selection of quasars

AU - Butler, Nathaniel

AU - Bloom, Joshua S.

PY - 2011/3

Y1 - 2011/3

N2 - We present a novel method for the optimal selection of quasars using time-series observations in a single photometric bandpass. Utilizing the damped random walk model of Kelly etal., we parameterize the ensemble quasar structure function in Sloan Stripe 82 as a function of observed brightness. The ensemble model fit can then be evaluated rigorously for and calibrated with individual light curves with no parameter fitting. This yields a classification in two statistics - one describing the fit confidence and the other describing the probability of a false alarm - which can be tuned, a priori, to achieve high quasar detection fractions (99% completeness with default cuts), given an acceptable rate of false alarms. We establish the typical rate of false alarms due to known variable stars as ≲3% (high purity). Applying the classification, we increase the sample of potential quasars relative to those known in Stripe 82 by as much as 29%, and by nearly a factor of two in the redshift range 2.5 < z < 3, where selection by color is extremely inefficient. This represents 1875 new quasars in a 290 deg2 field. The observed rates of both quasars and stars agree well with the model predictions, with >99% of quasars exhibiting the expected variability profile. We discuss the utility of the method at high redshift and in the regime of noisy and sparse data. Our time-series selection complements well-independent selection based on quasar colors and has strong potential for identifying high-redshift quasars for Baryon Acoustic Oscillations and other cosmology studies in the LSST era.

AB - We present a novel method for the optimal selection of quasars using time-series observations in a single photometric bandpass. Utilizing the damped random walk model of Kelly etal., we parameterize the ensemble quasar structure function in Sloan Stripe 82 as a function of observed brightness. The ensemble model fit can then be evaluated rigorously for and calibrated with individual light curves with no parameter fitting. This yields a classification in two statistics - one describing the fit confidence and the other describing the probability of a false alarm - which can be tuned, a priori, to achieve high quasar detection fractions (99% completeness with default cuts), given an acceptable rate of false alarms. We establish the typical rate of false alarms due to known variable stars as ≲3% (high purity). Applying the classification, we increase the sample of potential quasars relative to those known in Stripe 82 by as much as 29%, and by nearly a factor of two in the redshift range 2.5 < z < 3, where selection by color is extremely inefficient. This represents 1875 new quasars in a 290 deg2 field. The observed rates of both quasars and stars agree well with the model predictions, with >99% of quasars exhibiting the expected variability profile. We discuss the utility of the method at high redshift and in the regime of noisy and sparse data. Our time-series selection complements well-independent selection based on quasar colors and has strong potential for identifying high-redshift quasars for Baryon Acoustic Oscillations and other cosmology studies in the LSST era.

KW - cosmology: miscellaneous

KW - methods: statistical

KW - quasars: general

KW - stars: variables: general

UR - http://www.scopus.com/inward/record.url?scp=79953034518&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79953034518&partnerID=8YFLogxK

U2 - 10.1088/0004-6256/141/3/93

DO - 10.1088/0004-6256/141/3/93

M3 - Article

VL - 141

JO - Astronomical Journal

JF - Astronomical Journal

SN - 0004-6256

IS - 3

M1 - 93

ER -