论文标题
基于图像的可变星分类：光学引力透镜实验数据的首先结果
Image-based Classification of Variable Stars: First Results from Optical Gravitational Lensing Experiment Data
论文作者
论文摘要
最近，机器学习方法提出了一个可行的解决方案，用于在各个研究领域和业务应用程序中对基于图像的数据进行自动分类。科学家需要快速可靠的解决方案，以便能够处理始终增长的天文学数据。但是，到目前为止，天文学家一直在基于各种预计的统计和光曲线参数基于可变的恒星光曲线进行分类。在这项工作中，我们使用基于图像的卷积神经网络来对不同类型的可变星进行分类。我们使用OGLE-III调查中的相折叠光曲线的图像进行培训，验证和测试，并使用OGE-IV调查作为用于测试的独立数据集。在训练阶段之后，我们的神经网络能够分别在80％至99％之间将不同类型分类为OGLE-III和OGLE-IV的精度为77-98％。
Recently, machine learning methods presented a viable solution for automated classification of image-based data in various research fields and business applications. Scientists require a fast and reliable solution to be able to handle the always growing enormous amount of data in astronomy. However, so far astronomers have been mainly classifying variable star light curves based on various pre-computed statistics and light curve parameters. In this work we use an image-based Convolutional Neural Network to classify the different types of variable stars. We used images of phase-folded light curves from the OGLE-III survey for training, validating and testing and used OGLE-IV survey as an independent data set for testing. After the training phase, our neural network was able to classify the different types between 80 and 99%, and 77-98% accuracy for OGLE-III and OGLE-IV, respectively.
