The Sloan Digital Sky Survey Reverberation Mapping Project: Insights on Maximizing Efficiency in Lag Measurements and Black-Hole Masses

TL;DR

This study analyzes factors affecting the success of reverberation mapping in quasars, showing that optimized observing cadence and data quality significantly improve lag measurement efficiency for black hole mass estimation.

Contribution

It identifies key statistical and observational factors influencing lag detection success and demonstrates that reducing initial observing frequency can maintain high measurement efficiency.

Findings

Durbin-Watson statistic predicts lag detection success

Variability signal-to-noise ratio correlates with lag measurements

Reducing initial observations to 1.5 weeks retains 90% of lag measurements

Abstract

Multi-year observations from the Sloan Digital Sky Survey Reverberation Mapping (SDSS-RM) project have significantly increased the number of quasars with reliable reverberation-mapping lag measurements. We statistically analyze target properties, light-curve characteristics, and survey design choices to identify factors crucial for successful and efficient RM surveys. Analyzing 172 high-confidence ("gold") lag measurements from SDSS-RM for the H$\beta$, MgII, and CIV emission lines, we find that the Durbin-Watson statistic (a statistical test for residual correlation) is the most significant predictor of light curves suitable for lag detection. Variability signal-to-noise ratio and emission-line placement on the detector also correlate with successful lag measurements. We further investigate the impact of observing cadence on survey design by analyzing the effect of reducing observations in the first year of SDSS-RM. Our results demonstrate that a modest reduction in observing cadence to $\sim$1.5 weeks between observations can retain approximately 90% of the lag measurements compared to twice-weekly observations in the initial year. Provided similar and uniform sampling in subsequent years, this adjustment has a minimal effect on the overall recovery of lags across all emission lines. These results provide valuable inputs for optimizing future RM surveys.