Rotation-invariant convolutional neural networks for galaxy morphology prediction
Rotation-invariant convolutional neural networks for galaxy morphology prediction
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DOI：
10.1093/mnras/stv632
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发表时间：
2015-06-21
影响因子：
4.8
通讯作者：
Dambre, Joni
中科院分区：
文献类型：
--
作者：
Dieleman, Sander;Willett, Kyle W.;Dambre, Joni
测量星系的形态参数是研究其形成和演化的关键要求。像斯隆数字天空调查这样的调查已经产生了非常大的图像集合，这使得对星系形态的全人群分析成为可能。传统上，形态分析主要是由训练有素的专家进行目视检查，这很耗时，而且不会缩放到大量(大于或类似于10(4))的图像。尽管已经尝试建立自动分类系统，但仍未能达到预期的准确度水平。银河动物园项目成功地应用了众包策略，邀请在线用户通过回答一系列问题来对图像进行分类。不幸的是，即使是这种方法也不能很好地扩展到跟上越来越多的星系图像的可获得性。提出了一种利用平移和旋转对称性进行星系形态分类的深度神经网络模型。它是在银河挑战赛的背景下开发的，银河挑战赛是一项国际比赛，旨在根据银河动物园项目的注释图像建立最佳形态分类模型。对于银河动物园参与者之间高度一致的图像，我们的模型能够以近乎完美的准确率(>；99%)再现他们对大多数问题的共识。自信的模型预测非常准确，这使得该模型适合过滤大量图像，并将具有挑战性的图像转发给专家进行手动注释。这种方法在不影响准确度的情况下大大减少了专家的工作量。将这些算法应用于更大的训练数据集，对于分析诸如大型天气观测望远镜等未来调查的结果将是至关重要的。
Measuring the morphological parameters of galaxies is a key requirement for studying their formation and evolution. Surveys such as the Sloan Digital Sky Survey have resulted in the availability of very large collections of images, which have permitted population-wide analyses of galaxy morphology. Morphological analysis has traditionally been carried out mostly via visual inspection by trained experts, which is time consuming and does not scale to large (greater than or similar to 10(4)) numbers of images. Although attempts have been made to build automated classification systems, these have not been able to achieve the desired level of accuracy. The Galaxy Zoo project successfully applied a crowdsourcing strategy, inviting online users to classify images by answering a series of questions. Unfortunately, even this approach does not scale well enough to keep up with the increasing availability of galaxy images. We present a deep neural network model for galaxy morphology classification which exploits translational and rotational symmetry. It was developed in the context of the Galaxy Challenge, an international competition to build the best model for morphology classification based on annotated images from the Galaxy Zoo project. For images with high agreement among the Galaxy Zoo participants, our model is able to reproduce their consensus with near-perfect accuracy (> 99 per cent) for most questions. Confident model predictions are highly accurate, which makes the model suitable for filtering large collections of images and forwarding challenging images to experts for manual annotation. This approach greatly reduces the experts' workload without affecting accuracy. The application of these algorithms to larger sets of training data will be critical for analysing results from future surveys such as the Large Synoptic Survey Telescope.