Observational window effects on multi-object reverberation mapping. Issue 3 (13th August 2022)

• Record Type: Journal Article
• Authors: Malik, Umang; Sharp, Rob; Martini, Paul; Davis, Tamara M; Tucker, Brad E; Yu, Zhefu; Penton, Andrew; Lewis, Geraint F; Calcino, Josh
• Is Part Of: Monthly notices of the Royal Astronomical Society. Volume 516:Issue 3(2022)
• Page Start: 3238
• Page End: 3253
• Publication Date: 2022-08-13
• Subjects: methods: observational -- galaxies: active -- galaxies: nuclei -- quasars: emission lines; Astronomy -- Periodicals; Periodicals; 520.5
• DOI: 10.1093/mnras/stac2263
• Languages: English
• ISSNs: 0035-8711

Abstract

ABSTRACT: Contemporary reverberation mapping campaigns are employing wide-area photometric data and high-multiplex spectroscopy to efficiently monitor hundreds of active galactic nuclei (AGNs). 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 introduce 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 that 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 d. Using the Australian Dark Energy Survey as a baseline, we consider the implications of this analysis for the 4MOST/Time-Domain Extragalactic Survey campaign providing concurrent follow-up of the Legacy Survey of Space and Time deep-drilling fields, as well as upcoming programmes. 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. Optimizing the sample selection to fit the window function will improve survey efficacy.