论文标题
将信息理论应用于设计最佳过滤器的光度降期
Applying Information Theory to Design Optimal Filters for Photometric Redshifts
论文作者
论文摘要
在本文中，我们应用信息理论的想法来创建一种用于设计最佳过滤器的方法，以进行光度红移估计。我们展示了应用于一系列简单示例过滤器的方法，以激发光度红移估计器如何响应光度传递带的性能的直觉。然后，我们设计了一组逼真的六个过滤器，涵盖了光学波长，以优化$ z <= 2.3 $和$ i <25.3 $的光度红移。我们为这些最佳过滤器创建了模拟目录，并将过滤器与光度红移估算代码一起使用，以表明我们可以将光度红移误差的标准偏差提高7.1％，并提高了针对大型Synoptic Survey Telescope（LSST）的标准过滤器的9.9％，并将其提高9.9％。我们将最佳过滤器的功能与LSST进行比较，并发现LSST过滤器包含了最佳光度红移估计的关键功能。最后，我们描述了如何将信息理论应用于天文学中的一系列优化问题。
In this paper we apply ideas from information theory to create a method for the design of optimal filters for photometric redshift estimation. We show the method applied to a series of simple example filters in order to motivate an intuition for how photometric redshift estimators respond to the properties of photometric passbands. We then design a realistic set of six filters covering optical wavelengths that optimize photometric redshifts for $z <= 2.3$ and $i < 25.3$. We create a simulated catalog for these optimal filters and use our filters with a photometric redshift estimation code to show that we can improve the standard deviation of the photometric redshift error by 7.1% overall and improve outliers 9.9% over the standard filters proposed for the Large Synoptic Survey Telescope (LSST). We compare features of our optimal filters to LSST and find that the LSST filters incorporate key features for optimal photometric redshift estimation. Finally, we describe how information theory can be applied to a range of optimization problems in astronomy.
