Novel applications of machine learning in cosmology and beyond

机器学习在宇宙学及其他领域的新颖应用

• 批准号: 1966844
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1966844
• 关键词: Novel; applications; machine; learning; cosmology

With current surveys such as the Dark Energy Survey (DES), and in anticipation of even bigger projects like LSST and SKQ, the field of Astronomy is currently transitioning into an era dominated by enormous datasets. Indeed there will soon be more data than it would ever be possible to analyse, and although this influx of data could allow for a golden age of discovery, there is also the risk that the most interesting discoveries could be lost, drowned in the amount of data, never to be found. Machine learning is probably the best way we can address this problem, and in this project we aim to apply various machine learning algorithms to DES data to help analyse the data more efficiently and fuel discoveries

随着目前的调查，如暗能量调查（DES），并期待更大的项目，如LSST和SKQ，天文学领域目前正在过渡到一个由巨大的数据集主导的时代。事实上，很快就会有更多的数据，而这些数据将永远无法分析，尽管这些数据的涌入可能会带来一个发现的黄金时代，但也存在着最有趣的发现可能会丢失的风险，淹没在大量的数据中，永远不会被发现。机器学习可能是我们解决这个问题的最佳方法，在这个项目中，我们的目标是将各种机器学习算法应用于DES数据，以帮助更有效地分析数据并促进发现

项目成果

Machine Learning for Searching the Dark Energy Survey for Trans-Neptunian Objects

用于搜索海王星外天体暗能量巡天的机器学习

• DOI: 10.1088/1538-3873/abcaea
• 发表时间: 2020
• 期刊: Publications of the Astronomical Society of the Pacific
• 影响因子: 3.5
• 作者: Henghes B