测光红移估计的机器学习方法的基准测试和可扩展性
Benchmarking and Scalability of Machine Learning Methods for Photometric Redshift Estimation
摘要：
获得精确的测光红移估计是宇宙学的一个重要方面，仍然是许多分析的先决条件。在创造新的方法来产生红移估计时，已经转向使用机器学习技术。然而，人们并没有太多地关注不同的机器学习方法如何随着不断增加的数据量而扩展或执行。在这里，我们介绍了一个基准测试，旨在分析不同的监督机器学习方法的性能和可扩展性的光度红移估计。利用斯隆数字巡天（SDSS-DR 12）数据集，我们分析了各种最常用的机器学习算法。通过将用于训练和测试算法的星系数量扩展到100万个，我们获得了几个指标，证明了算法在这项任务中的性能和可扩展性。此外，通过引入一种新的优化方法，考虑时间的优化，我们能够证明如何一个小的让步的错误可以允许在效率上有很大的提高。从测试的算法中，我们发现随机森林在误差方面表现最好，均方误差为MSE = 0.0042;然而，由于其他算法（如Boosted Decision Trees和k-Nearest Neighbours）的表现非常相似，我们使用我们的基准测试来证明不同的算法在不同的场景中是如何优越的。我们相信这样的基准将在即将到来的调查中变得更加重要，例如LSST，它将捕获数十亿个需要光度红移的星系。
英文摘要：
Obtaining accurate photometric redshift estimations is an important aspect of cosmology, remaining a prerequisite of many analyses. In creating novel methods to produce redshift estimations, there has been a shift towards using machine learning techniques. However, there has not been as much of a focus on how well different machine learning methods scale or perform with the ever-increasing amounts of data being produced. Here, we introduce a benchmark designed to analyse the performance and scalability of different supervised machine learning methods for photometric redshift estimation. Making use of the Sloan Digital Sky Survey (SDSS - DR12) dataset, we analysed a variety of the most used machine learning algorithms. By scaling the number of galaxies used to train and test the algorithms up to one million, we obtained several metrics demonstrating the algorithms' performance and scalability for this task. Furthermore, by introducing a new optimisation method, time-considered optimisation, we were able to demonstrate how a small concession of error can allow for a great improvement in efficiency. From the algorithms tested we found that the Random Forest performed best in terms of error with a mean squared error, MSE = 0.0042; however, as other algorithms such as Boosted Decision Trees and k-Nearest Neighbours performed incredibly similarly, we used our benchmarks to demonstrate how different algorithms could be superior in different scenarios. We believe benchmarks such as this will become even more vital with upcoming surveys, such as LSST, which will capture billions of galaxies requiring photometric redshifts.
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