AstroML: Machine Learning for Astrophysics

天文学已经进入了一个海量数据流的时代，包含数以千计的时间步长测量的数亿颗恒星和星系的星表--以及数百个需要分析的属性。为了从这些庞大而复杂的数据集中提取知识，我们必须考虑噪音和差距，并了解我们是否以及何时可能检测到了一种全新的物理现象。问题不仅仅是数据的大小，而是一个基本的问题，即如何发现、表示、可视化这些数据所包含的知识并与其交互。天文数据为开发适用于整个物理和生命科学的方法提供了一个广受欢迎的试验台。AsterML是一个开源的机器学习库，它解决了所有的挑战，提供了一个公开可用的存储库，用于快速实现天文学统计例程，以及使用统计学和机器学习技术进行天体物理数据分析的例子。在发布后的三年里，Astar ML已经被安装了超过21,000次。目前的项目将进一步将AsterML发展成为下一代天体物理测量的通用机器学习工具包，增加代码示例和教程，开发多核和多处理硬件，并支持文本《天文学中的统计、数据挖掘和机器学习：测量数据分析实用Python指南》的第二版。将要开发的算法包括近似贝叶斯计算、分层贝叶斯、深度学习算法的接口，以及修改回归和正则化代码以解决数据中的不确定性。所有开发的算法都将公开可用，Astar ML已经用于癌症研究和证券市场分析，并教授天文学中的数据科学。重构后的代码可用于教授大规模机器学习所需的统计学和软件工程技术。

Astronomy has entered an era of massive data streams, with catalogs containing hundreds of millions of stars and galaxies measured at thousands of time-steps with hundreds of attributes to be analyzed. To extract knowledge from these large and complex data sets we must account for noise and gaps, and understand if and when we may have detected a fundamentally new physical phenomenon. The problem is not solely the size of the data, but a basic question of how to discover, represent, visualize and interact with the knowledge that these data contain. Astronomical data provide a popular testbed for developing methods applicable throughout the physical and life sciences.astroML is an open source machine-learning library that addresses all of the challenges, providing a publicly available repository for fast python implementations of statistical routines for astronomy, as well as examples of astrophysical data analyses using techniques from statistics and machine learning. In the three years since its release, astroML has been installed over 21,000 times. The current project will further develop astroML into a general machine learning toolkit for the next generation of astrophysical surveys, adding code examples and tutorials, exploiting multicore and multiprocessing hardware, and supporting the second edition of the text "Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data". Algorithms to be developed include approximate Bayesian computation, hierarchical Bayes, an interface to deep learning algorithms, and modifying the regression and regularization code to account for uncertainties within the data.All developed algorithms will be publicly available, and astroML has already been used in cancer research and analysis of the securities market, and to teach data science in astronomy. The refactored code can be used to teach both the statistics and software engineering techniques needed for large scale machine learning.