We compute the distribution of likelihoods from the non-parametric iterative smoothing method over a set of mock Pantheon-like type Ia supernova datasets. We use this likelihood distribution to test whether typical dark energy models are consistent with the data and to perform parameter estimation. In this approach, the consistency of a model and the data is determined without the need for comparison with another alternative model. Simulating future WFIRST-like data, we study type II errors and show how confidently we can distinguish different dark energy models using this non-parametric approach.

中文翻译：

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使用迭代平滑法的模型选择和参数估计

我们在一组模拟 Pantheon 类 Ia 型超新星数据集上计算非参数迭代平滑方法的似然分布。我们使用这种似然分布来测试典型的暗能量模型是否与数据一致并进行参数估计。在这种方法中，无需与另一个替代模型进行比较即可确定模型和数据的一致性。模拟未来类似 WFIRST 的数据，我们研究 II 类错误，并展示我们可以多么自信地使用这种非参数方法区分不同的暗能量模型。