论文标题
使用深度学习检测哈勃太空望远镜图像中的小行星步道
Detection of asteroid trails in Hubble Space Telescope images using Deep Learning
论文作者
论文摘要
我们提出了深度学习的应用，以识别哈勃太空望远镜拍摄的单曝光照片中的小行星小径。使用基于多层深卷积神经网络的算法，我们报告验证集的精度高于80％。我们的项目是由Hubble小行星猎人项目的动机，该项目的重点是识别这些对象，以便本地化和更好地表征它们。我们的目的是证明机器学习技术在尝试解决与天文学和天体物理学密切相关的问题方面非常有用，但是它们仍然不足以进行非常具体的任务。
We present an application of Deep Learning for the image recognition of asteroid trails in single-exposure photos taken by the Hubble Space Telescope. Using algorithms based on multi-layered deep Convolutional Neural Networks, we report accuracies of above 80% on the validation set. Our project was motivated by the Hubble Asteroid Hunter project on Zooniverse, which focused on identifying these objects in order to localize and better characterize them. We aim to demonstrate that Machine Learning techniques can be very useful in trying to solve problems that are closely related to Astronomy and Astrophysics, but that they are still not developed enough for very specific tasks.
