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Deep Learning for Classification of Astronomical Archives

天文档案分类的深度学习

基本信息

批准号：
ST/R006768/1
负责人：
Albert Zijlstra
金额：
$ 10.86万
依托单位：
University of Manchester
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2019
资助国家：
英国
起止时间：
2019 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=ST%2FR006768%2F1
关键词：
Deep Learning Classification Astronomical Archives

项目摘要

Astronomy is known for producing highly appealing images which often get published and re-published world-wide. HST images, for instance, have appeared on web sites, news papers, and even stamps. Less well known is that these images are the tip of the ice berg. The large majority of images, even from HST, do not contain such pretty structures. Astronomers locate the news-worthy ones by manual inspection of all the data.Modern astronomical instruments provide far more data than can even be manually inspected. ALMA, for instance, can produce over 10,000 images from a single observation. Each of these measures a slightly different wavelength of radiation, and many of the images will be empty or near-empty. But without looking at each one, how does the astronomer know which images to select? This project will look at techniques to let computers do the work. They can investigate all images much faster than people can, and can learn which ones are of interest by looking for characteristic patterns. The problem is that they need to be taught what is 'characteristic' something even astronomers may find difficult to put into an algorithm. Malaysian scientists have unparalleled expertise in image classification using deep learning techniques, and in semantic descriptions. This expertise will be brought to the astronomical archives, to develop techniques of computer learning to aid astronomers. Manchester is one of the access points to the ALMA archive. The combination of the data and astrophysical expertise in Manchester and image classification by deep learning in Malaysia will provide new and powerful tools for science.

天文学以产生极具吸引力的图像而闻名，这些图像经常在世界范围内发表和重发。例如，HST图像已经出现在网站、报纸甚至邮票上。不太为人所知的是，这些图像只是冰山一角。大多数图像，即使是来自HST的图像，也不包含这样漂亮的结构。天文学家通过人工检查所有数据来定位有新闻价值的数据。现代天文仪器提供的数据远远超过人工检查的数据。例如，ALMA可以从一次观测中产生超过10,000张图像。每一种测量的辐射波长都略有不同，许多图像将是空的或接近空的。但是，如果不看每一幅图像，天文学家如何知道应该选择哪些图像呢？这个项目将着眼于让计算机完成这项工作的技术。他们可以比人类更快地研究所有图像，并可以通过寻找特征模式来了解哪些是感兴趣的。问题是，他们需要被教导什么是“特征”，即使是天文学家可能也很难将其纳入算法中。马来西亚科学家在使用深度学习技术的图像分类和语义描述方面拥有无与伦比的专业知识。这些专业知识将被带到天文档案馆，以开发计算机学习技术，以帮助天文学家。曼彻斯特是ALMA档案的访问点之一。曼彻斯特的数据和天体物理专业知识与马来西亚的深度学习图像分类相结合，将为科学提供新的强大工具。

项目成果

期刊论文数量（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Classification of Planetary Nebulae through Deep Transfer Learning

DOI：
10.3390/galaxies8040088
发表时间：
2020-12
期刊：
Galaxies
影响因子：
2.5
作者：
Dayang N. F. Awang Iskandar;A. Zijlstra;I. McDonald;R. Abdullah;G. Fuller;A. Fauzi;Johari Abdullah
通讯作者：
Dayang N. F. Awang Iskandar;A. Zijlstra;I. McDonald;R. Abdullah;G. Fuller;A. Fauzi;Johari Abdullah

First deep images catalogue of extended IPHAS PNe

扩展 IPHAS PNe 的第一个深度图像目录