Observational window effects on multi-object Reverberation Mapping

TL;DR

This paper analyzes how observational window effects, such as cadence and seasonal gaps, impact the ability to recover reverberation lags in AGN monitoring campaigns, emphasizing the importance of survey design and baseline duration.

Contribution

It provides a comprehensive analysis of how survey window functions affect reverberation lag recovery and offers strategies for optimizing observational campaigns.

Findings

Seasonal gaps significantly hinder lag recovery for lags above 100 days.

Extending the survey baseline improves lag recovery success.

Optimizing sample selection relative to the window function enhances survey efficacy.

Abstract

Contemporary reverberation mapping campaigns are employing wide-area photometric data and high-multiplex spectroscopy to efficiently monitor hundreds of active galactic nuclei (AGN). However, the interaction of the window function(s) imposed by the observation cadence with the reverberation lag and AGN variability time scales (intrinsic to each source over a range of luminosities) impact our ability to recover these fundamental physical properties. Time dilation effects due to the sample source redshift distribution introduces added complexity. We present comprehensive analysis of the implications of observational cadence, seasonal gaps and campaign baseline duration (i.e., the survey window function) for reverberation lag recovery. We find the presence of a significant seasonal gap dominates the efficacy of any given campaign strategy for lag recovery across the parameter space, particularly for those sources with observed-frame lags above 100 days. Using the OzDES survey as a baseline, we consider the implications of this analysis for the 4MOST/TiDES campaign providing concurrent follow-up of the LSST deep-drilling fields, as well as upcoming programs. We conclude that the success of such surveys will be critically limited by the seasonal visibility of some potential field choices, but show significant improvement from extending the baseline. Optimising the sample selection to fit the window function will improve survey efficacy.