为模型选择目标优化未来的暗能量调查
Optimizing future dark energy surveys for model selection goals
摘要：
我们展示了一种方法来优化未来的暗能量调查的能力，以回答模型选择的问题，如"是由于宇宙学常数或动态暗能量模型的加速度？'.模型选择的品质因数的定义，利用贝叶斯因子，并通过蒙特卡洛方法在其设计参数空间的调查优化。作为一个具体的例子，我们将我们的方法，通用的多纤维重子声学振荡光谱调查，提出的SuMIRe PFS相媲美，目前的实施方案的基础上的萨维奇-迪基密度比是准确和实用的优化使用。结果表明，虽然使用模型选择的最佳调查同意使用暗能量任务组（DETF）图的优点，他们提供了更好的通知调查配置的灵活性和性能的绝对规模，例如，我们发现调查配置接近最佳模型选择性能，尽管其相应的DETF图的优点是在其最大值的50%。这种贝叶斯因子方法使我们能够解释调查配置，这对于手头的任务来说是足够好的，特别是当想要添加额外的科学目标以及在同一项目中处理时间限制或多个探测器时，这一点至关重要。
英文摘要：
We demonstrate a methodology for optimizing the ability of future dark energy surveys to answer model selection questions, such as `Is acceleration due to a cosmological constant or a dynamical dark energy model?'. Model selection Figures of Merit are defined, exploiting the Bayes factor, and surveys optimized over their design parameter space via a Monte Carlo method. As a specific example we apply our methods to generic multi-fibre baryon acoustic oscillation spectroscopic surveys, comparable to that proposed for SuMIRe PFS, and present implementations based on the Savage-Dickey Density Ratio that are both accurate and practical for use in optimization. It is shown that whilst the optimal surveys using model selection agree with those found using the Dark Energy Task Force (DETF) Figure of Merit, they provide better informed flexibility of survey configuration and an absolute scale for performance; for example, we find survey configurations with close to optimal model selection performance despite their corresponding DETF Figure of Merit being at only 50% of its maximum. This Bayes factor approach allows us to interpret the survey configurations that will be good enough for the task at hand, vital especially when wanting to add extra science goals and in dealing with time restrictions or multiple probes within the same project.
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