类星体光学可变性：寻找带间时间延迟
Quasar optical variability: searching for interband time delays
摘要：
目标。本文的主要目的是研究类星体样品中不同波段光变化之间的时间延迟。测量大量类星体的可靠时间延迟可能有助于限制其中心引擎的模型。标准的吸积盘辐射模型预测了较长波长在较短波长之后的延迟，这种延迟取决于类星体的基本参数。由于黑洞的质量和吸积率对于我们使用的样本是近似已知的，所以可以将观察到的时间延迟与预期的时间延迟进行比较。方法.我们应用内插交叉关联函数（ICCF）方法对Giveon等人的42个类星体的样本进行了分析，用两种颜色（B和R）进行监测，以找到ICCF峰所代表的时间滞后。进行了不同的测试以评估光度误差、采样等的影响，对最终的结果。结果我们发现，大多数物体的红光曲线在蓝光曲线之后显示出延迟（正滞后），样本的平均延迟时间约为+4天（中位数为+3天），尽管散射很明显。这些结果与后处理模型基本一致，特别是对于采样良好的物体。标准化的时滞偏差似乎与其他类星体的性质没有显著的相关性，包括光学，无线电或X射线可测量。另一方面，许多物体显示出明显的负滞后，如果这是真的，可能会对可变性模型产生重要影响。
英文摘要：
Aims. The main purpose of this paper is to study time delays between the light variations in different wavebands for a sample of quasars. Measuring a reliable time delay for a large number of quasars may help constraint the models of their central engines. The standard accretion disk irradiation model predicts a delay of the longer wavelengths behind the shorter ones, a delay that depends on the fundamental quasar parameters. Since the black hole masses and the accretion rates are approximately known for the sample we use, one can compare the observed time delays with the expected ones. Methods. We applied the interpolation cross-correlation function (ICCF) method to the Giveon et al. sample of 42 quasars, monitored in two (B and R) colors, to find the time lags represented by the ICCF peaks. Different tests were performed to assess the influence of photometric errors, sampling, etc., on the final result. Results. We found that most of the objects show a delay in the red light curve behind the blue one (a positive lag), which on average for the sample is about +4 days (+3 for the median), although the scatter is significant. These results are broadly consistent with the reprocessing model, especially for the well-sampled objects. The normalized time-lag deviations do not seem to correlate significantly with other quasar properties, including optical, radio, or X-ray measurables. On the other hand, many objects show a clear negative lag, which, if real, may have important consequences for the variability models.
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