标题： 穷尽信息：光谱红移PDF的新型贝叶斯组合 显示英文标题

标题： Exhausting the Information: Novel Bayesian Combination of Photometric Redshift PDFs

摘要： 近年来，测光红移概率密度函数（photo-$z$ PDFs）的估算与利用变得越来越重要，目前已有各种各样的算法来计算 photo-$z$，每种方法都有其优缺点。 本文提出了一种新颖且高效的贝叶斯框架，通过最大化测光数据的信息，将不同 photo-$z$技术的结果结合起来，得到更强大和稳健的估计。 为了展示这一点，我们使用了基于随机森林的监督机器学习技术、一种基于自组织映射的无监督方法，以及一种标准模板拟合方法，但可以轻松扩展到其他现有技术。 我们使用来自 DEEP2 和 SDSS 调查的数据来探索结合这些技术预测的不同方法。 通过使用不同的性能指标，我们证明我们可以提高最终 photo-$z$估计的准确性，减少异常值的比例，并且当我们将朴素贝叶斯分类器应用于这种综合信息时，异常值的识别显著改善。 我们更稳健和准确的 photo-$z$ PDFs 将允许通过对当前和未来的测光巡天来做出更加精确的宇宙学限制。 随着我们分析的测光数据逐渐接近甚至超出可用训练数据的极限（这将是大型综合巡天望远镜的情况），这些改进至关重要。 显示英文摘要

摘要： The estimation and utilization of photometric redshift probability density functions (photo-$z$ PDFs) has become increasingly important over the last few years and currently there exist a wide variety of algorithms to compute photo-$z$'s, each with their own strengths and weaknesses. In this paper, we present a novel and efficient Bayesian framework that combines the results from different photo-$z$ techniques into a more powerful and robust estimate by maximizing the information from the photometric data. To demonstrate this we use a supervised machine learning technique based on random forest, an unsupervised method based on self-organizing maps, and a standard template fitting method but can be easily extend to other existing techniques. We use data from the DEEP2 and the SDSS surveys to explore different methods for combining the predictions from these techniques. By using different performance metrics, we demonstrate that we can improve the accuracy of our final photo-$z$ estimate over the best input technique, that the fraction of outliers is reduced, and that the identification of outliers is significantly improved when we apply a Na\"{\i}ve Bayes Classifier to this combined information. Our more robust and accurate photo-$z$ PDFs will allow even more precise cosmological constraints to be made by using current and future photometric surveys. These improvements are crucial as we move to analyze photometric data that push to or even past the limits of the available training data, which will be the case with the Large Synoptic Survey Telescope.