The Sloan Digital Sky Survey Reverberation Mapping Project: Comparison of Lag Measurement Methods with Simulated Observations
AuthorLi, Jennifer I-Hsiu
Brandt, W. N.
Grier, C. J.
Hall, P. B.
Ho, L. C.
Schneider, D. P.
Trump, J. R.
Starkey, D. A.
AffiliationUniv Arizona, Steward Observ
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PublisherIOP PUBLISHING LTD
CitationJennifer I-Hsiu Li et al 2019 ApJ 884 119
RightsCopyright © 2019. The American Astronomical Society. All rights reserved.
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at firstname.lastname@example.org.
AbstractWe investigate the performance of different methodologies that measure the time lag between broad-line and continuum variations in reverberation mapping data using simulated light curves that probe a range of cadence, time baseline, and signal-to-noise ratio in the flux measurements. We compare three widely adopted lag-measuring methods: the interpolated cross-correlation function (ICCF), the z-transformed discrete correlation function (ZDCF), and the Markov chain Monte Carlo code JAVELIN, for mock data with qualities typical of multiobject spectroscopic reverberation mapping (MOS-RM) surveys that simultaneously monitor hundreds of quasars. We quantify the overall lag-detection efficiency, the rate of false detections, and the quality of lag measurements for each of these methods and under different survey designs (e.g., observing cadence and depth) using mock quasar light curves. Overall JAVELIN and ICCF outperform ZDCF in essentially all tests performed. Compared with ICCF, JAVELIN produces higher quality lag measurements, is capable of measuring more lags with timescales shorter than the observing cadence, is less susceptible to seasonal gaps and signal-to-noise ratio degradation in the light curves, and produces more accurate lag uncertainties. We measure the Hβ broad-line region size–luminosity (R–L) relation with each method using the simulated light curves to assess the impact of selection effects of the design of MOS-RM surveys. The slope of the R–L relation measured by JAVELIN is the least biased among the three methods and is consistent across different survey designs. These results demonstrate a clear preference for JAVELIN over the other two nonparametric methods for MOS-RM programs, particularly in the regime of limited light-curve quality as expected from most MOS-RM programs.
VersionFinal published version
SponsorsAlfred P. Sloan Research FellowshipAlfred P. Sloan Foundation; NSFNational Science Foundation (NSF) [AST-1715579, AST-1517113, AST-1516784]; National Science Foundation of ChinaNational Natural Science Foundation of China ; National Key R&D Program of China [2016YFA0400702]; NASANational Aeronautics & Space Administration (NASA) [HST-GO-15260]; Alfred P. Sloan FoundationAlfred P. Sloan Foundation; National Science FoundationNational Science Foundation (NSF); U.S. Department of Energy Office of ScienceUnited States Department of Energy (DOE); University of Arizona; Brazilian Participation Group; Brookhaven National LaboratoryUnited States Department of Energy (DOE); University of CambridgeUniversity of Cambridge; Carnegie Mellon University; University of FloridaUniversity of Florida; French Participation Group; German Participation Group; Harvard University; Instituto de Astrofisica de Canarias; Michigan State/Notre Dame/JINA Participation Group; Johns Hopkins UniversityJohns Hopkins University; Lawrence Berkeley National LaboratoryUnited States Department of Energy (DOE); Max Planck Institute for Astrophysics; Max Planck Institute for Extraterrestrial Physics; New Mexico State University; New York University; Ohio State UniversityOhio State University; Pennsylvania State University; University of Portsmouth; Princeton UniversityPrinceton University; Spanish Participation Group; University of Tokyo; University of Utah; Vanderbilt University; University of Virginia; University of WashingtonUniversity of Washington; Yale University