AstroML: Machine Learning for Astrophysics

AstroML：天体物理学机器学习

• 批准号: 1715122
• 负责人: Andrew Connolly
• 金额: $ 39.89万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-15 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1715122&HistoricalAwards=false
• 关键词: AstroML; Machine; Learning; Astrophysics

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.

天文学已经进入了一个大规模数据流的时代，目录包含数以亿计的恒星和星系，在数千个时间步测量，有数百个属性需要分析。 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一种全新的物理现象。 问题不仅仅是数据的大小，而是如何发现、表示、可视化这些数据所包含的知识并与之交互的基本问题。 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受欢迎的测试平台。astroML是一个开源机器学习库，可以解决所有挑战，为天文学统计例程的快速Python实现提供了一个公开可用的存储库，以及使用统计和机器学习技术进行天体物理数据分析的示例。 自发布以来的三年中，astroML已被安装超过21，000次。 目前的项目将进一步开发astroML成为下一代天体物理调查的通用机器学习工具包，增加代码示例和教程，利用多核和多处理硬件，并支持第二版的文本“统计，数据挖掘和天文学机器学习：调查数据分析的实用Python指南”。 我们将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考虑数据中的不确定性。所有开发的算法都将公开，astroML已经用于癌症研究和证券市场分析，并用于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和软件工程技术。

项目成果

The SunPy Project: Open Source Development and Status of the Version 1.0 Core Package

• DOI: 10.3847/1538-4357/ab4f7a
• 发表时间: 2020-02-10
• 期刊: ASTROPHYSICAL JOURNAL
• 影响因子: 4.9
• 作者: Barnes, Will T.;Bobra, Monica G.;Dang, Trung Kien
• 通讯作者: Dang, Trung Kien

Robust Period Estimation Using Mutual Information for Multiband Light Curves in the Synoptic Survey Era

• DOI: 10.3847/1538-4365/aab77c
• 发表时间: 2017-09
• 期刊: The Astrophysical Journal Supplement Series
• 作者: P. Huijse;P. Estévez;F. Förster;S. Daniel;A. Connolly;P. Protopapas;R. Carrasco;J. Príncipe

Sifting through the Static: Moving Object Detection in Difference Images

筛选静态：差异图像中的运动物体检测

• DOI: 10.3847/1538-3881/ac22ff
• 发表时间: 2021
• 期刊: The Astronomical Journal
• 作者: Smotherman, Hayden;Connolly, Andrew J.;Kalmbach, J. Bryce;Portillo, Stephen K.;Bektesevic, Dino;Eggl, Siegfried;Juric, Mario;Moeyens, Joachim;Whidden, Peter J.
• 通讯作者: Whidden, Peter J.

Optimization of the Observing Cadence for the Rubin Observatory Legacy Survey of Space and Time: A Pioneering Process of Community-focused Experimental Design

鲁宾天文台遗产时空巡天观测节奏的优化：以社区为中心的实验设计的开创性过程

• DOI: 10.3847/1538-4365/ac3e72
• 发表时间: 2021
• 期刊: The Astrophysical Journal Supplement Series
• 作者: Bianco, Federica B.;Ivezić, Željko;Jones, R. Lynne;Graham, Melissa L.;Marshall, Phil;Saha, Abhijit;Strauss, Michael A.;Yoachim, Peter;Ribeiro, Tiago;Anguita, Timo
• 通讯作者: Anguita, Timo

Dimensionality Reduction of SDSS Spectra with Variational Autoencoders

• DOI: 10.3847/1538-3881/ab9644
• 发表时间: 2020-02
• 期刊: The Astronomical Journal
• 作者: S. Portillo;J. Parejko;J. Vergara;A. Connolly