类星体微变检验的可靠性
On the reliability of microvariability tests in quasars
摘要：
微变探测类星体的物理和内部结构。不可预测性和微小的通量变化使这种现象难以捉摸，难以检测。基于方差的探测，如C和F检验，或两者的结合，是比较类星体和比较星的光变曲线的流行方法。最近，在一些研究中的检测声明依赖于C和F检验的结果的一致性，或两个F检验的实例，在拒绝无变异零假设。然而，C检验是一种不可靠的统计程序，F检验不稳健，并且具有并发结果的检验组合绝不是一种简单的方法。先验功效分析计算和蒙特卡罗模拟的事后分析显示，方差分析检验在检测微小变异方面具有良好的一致性，同时也显示了F检验的局限性。此外，组合检验产生的相关概率使得统计显著性的评估不可行。然而，可以包括来自多个场星的数据，以提高单个F检验的功效，从而提高统计分析的可靠性。当有几个比较之星可用时，这将是首选方法。给出了一个使用两颗星和增强的F检验的例子。这些结果表明使用适当的方法的重要性，并避免不适当的程序，可能会危及微变异检测。功率分析和蒙特卡罗模拟是研究规划的有用工具，因为它们可以揭示不同研究方法的鲁棒性和可靠性。
英文摘要：
Microvariations probe the physics and internal structure of quasars. Unpredictability and small flux variations make this phenomenon elusive and difficult to detect. Variance based probes such as the C and F tests, or a combination of both, are popular methods to compare the light-curves of the quasar and a comparison star. Recently, detection claims in some studies depend on the agreement of the results of the C and F tests, or of two instances of the F-test, in rejecting the non-variation null hypothesis. However, the C-test is a non-reliable statistical procedure, the F-test is not robust, and the combination of tests with concurrent results is anything but a straightforward methodology. A priori Power Analysis calculations and post hoc analysis of Monte-Carlo simulations show excellent agreement for the Analysis of Variance test to detect microvariations, as well as the limitations of the F-test. Additionally, combined tests yield correlated probabilities that make the assessment of statistical significance unworkable. However, it is possible to include data from several field stars to enhance the power in a single F-test, increasing the reliability of the statistical analysis. This would be the preferred methodology when several comparison stars are available. An example using two stars and the enhanced F-test is presented. These results show the importance of using adequate methodologies, and avoid inappropriate procedures that can jeopardize microvariability detections. Power analysis and Monte-Carlo simulations are useful tools for research planning, as they can reveal the robustness and reliability of different research approaches.
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