Deep Learning for Classification of Astronomical Archives

天文学以产生高度吸引人的图像而闻名，这些图像经常在世界范围内出版和重新出版。例如，HST的图像已经出现在网站、报纸甚至邮票上。鲜为人知的是，这些图像是贝格的尖端。绝大多数图像，即使来自HST，也不包含如此漂亮的结构。天文学家通过人工检查所有数据来确定有新闻价值的天体。现代天文仪器提供的数据远远超过人工检查。例如，阿尔马可以从一次观察中产生超过10，000张图像。每一个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，如果不看每一个，天文学家如何知道选择哪些图像？这个项目将着眼于让计算机完成这项工作的技术。它们可以比人类更快地调查所有图像，并可以通过寻找特征模式来了解哪些图像是感兴趣的。问题是，他们需要被教导什么是“特征”，即使是天文学家也很难将其放入算法中。马来西亚科学家在使用深度学习技术进行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来帮助天文学家。曼彻斯特是阿尔马档案馆的入口之一。曼彻斯特的数据和天体物理学专业知识与马来西亚深度学习的图像分类相结合，将为科学提供新的强大工具。

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.