设计决定性的检测
Designing Decisive Detections
摘要：
我们提出了一个一般的贝叶斯形式主义的定义的数字的优点（FOMs）量化的科学回报的未来实验。我们介绍了两个新的FOM为未来的实验的基础上，他们的模型选择能力，称为实验的决定性和预期的证据强度。我们通过考虑暗能量探测器来说明这些，并比较了第二阶段、第三阶段和第四阶段暗能量探测器的相对优点。我们发现，基于超新星和弱透镜的探测器在模型选择任务上的表现比暗能量任务组定义的Fisher矩阵FoM所示的要好。我们认为，我们的能力，以优化未来的实验暗能量模型选择的目标是有限的，我们目前的不确定性的模型和它们的参数，这是忽略了通常的Fisher矩阵预测。我们的方法提供了一个更现实的评估未来的探测器的能力，可以应用在各种情况下。
英文摘要：
We present a general Bayesian formalism for the definition of Figures of Merit (FoMs) quantifying the scientific return of a future experiment. We introduce two new FoMs for future experiments based on their model selection capabilities, called the decisiveness of the experiment and the expected strength of evidence. We illustrate these by considering dark energy probes, and compare the relative merits of stage II, III and IV dark energy probes. We find that probes based on supernovae and on weak lensing perform rather better on model selection tasks than is indicated by their Fisher matrix FoM as defined by the Dark Energy Task Force. We argue that our ability to optimize future experiments for dark energy model selection goals is limited by our current uncertainty over the models and their parameters, which is ignored in the usual Fisher matrix forecasts. Our approach gives a more realistic assessment of the capabilities of future probes and can be applied in a variety of situations.
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