The Performance of Photometric Reverberation Mapping at High Redshift and the Reliability of Damped Random Walk Models

TL;DR

This study demonstrates that photometric reverberation mapping at high redshift can reliably estimate black hole masses with high efficiency, even when the Damped Random Walk model assumptions are not fully valid.

Contribution

We evaluate the effectiveness of photometric reverberation mapping at higher redshifts and assess the reliability of the Damped Random Walk model through extensive simulations.

Findings

Recovered lag within 6% accuracy under good signal-to-noise conditions

Deconvolved aliases to improve lag signal-to-noise by a factor of 2.2

Achieved 200% higher SNR efficiency compared to SDSS-RM with less observing time

Abstract

Accurate methods for reverberation mapping using photometry are highly sought after since they are inherently less resource intensive than spectroscopic techniques. However, the effectiveness of photometric reverberation mapping for estimating black hole masses is sparsely investigated at redshifts higher than $z\approx0.04$. Furthermore, photometric methods frequently assume a Damped Random Walk (DRW) model, which may not be universally applicable. We perform photometric reverberation mapping using the Javelin photometric DRW model for the QSO SDSSJ144645.44+625304.0 at z=0.351 and estimate the H$\beta$ lag of $65^{+6}_{-1}$ days and black-hole mass of $10^{8.22^{+0.13}_{-0.15}}M_{\odot}$. An analysis of the reliability of photometric reverberation mapping, conducted using many thousands of simulated CARMA process light-curves, shows that we can recover the input lag to within 6 per cent on average given our target's observed signal-to-noise of > 20 and an average cadence of 14 days (even when DRW is not applicable). Furthermore, we use our suite of simulated light curves to deconvolve aliases and artefacts from our QSO's posterior probability distribution, increasing the signal-to-noise on the lag by a factor of $\sim2.2$. We exceed the signal-to-noise of the Sloan Digital Sky Survey Reverberation Mapping Project (SDSS-RM) campaign with a quarter of the observing time per object, resulting in a $\sim200$ per cent increase in SNR efficiency over SDSS-RM.