论文标题

观察窗口对多对象混响映射的影响

Observational window effects on multi-object Reverberation Mapping

论文作者

Malik, Umang, Sharp, Rob, Martini, Paul, Davis, Tamara M., Tucker, Brad E., Yu, Zhefu, Penton, Andrew, Lewis, Geraint F., Calcino, Josh

论文摘要

当代的混响映射活动正在采用广泛的面积光度数据和高型材光谱,以有效监测数百个活跃的银河核(AGN)。然而,观察节奏与回响滞后和AGN变异时间尺度(在一系列亮度范围内固有的)施加的窗口函数的相互作用会影响我们恢复这些基本物理特性的能力。由于样品源红移分布引起的时间扩张效应带来了增加的复杂性。我们介绍了观察性节奏,季节性差距和竞选基线持续时间(即调查窗口功能)对混响滞后恢复的含义的全面分析。我们发现存在重大季节性差距的存在主要占主导地位的任何给定竞选策略在整个参数空间中恢复的滞后策略的功效,尤其是对于那些观察到的固定滞后滞后的来源以上100天以上。使用OZDES调查作为基准,我们考虑了此分析对4个/潮汐活动的含义,从而提供了LSST深入领域的同时随访以及即将到来的计划。我们得出的结论是,此类调查的成功将受到某些潜在现场选择的季节性可见性的严重限制,但与延长基线相比,这表现出显着改善。优化样品选择以适合窗口功能将提高调查功效。

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.

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