变星图像分类：光学引力透镜实验数据的初步结果
Image-based Classification of Variable Stars: First Results from Optical Gravitational Lensing Experiment Data
摘要：
最近，机器学习方法为各种研究领域和商业应用中基于图像的数据的自动分类提供了可行的解决方案。科学家需要一个快速可靠的解决方案，以便能够处理不断增长的天文学数据。然而，到目前为止，天文学家主要是根据各种预先计算的统计数据和光变曲线参数对变星的光变曲线进行分类。在这项工作中，我们使用基于图像的卷积神经网络来分类不同类型的变星。我们使用OGLE-III调查的相位折叠光变曲线图像进行训练、验证和测试，并使用OGLE-IV调查作为独立的数据集进行测试。在训练阶段之后，我们的神经网络能够对OGLE-III和OGLE-IV的不同类型进行80%至99%的分类，准确率分别为77%至98%。
英文摘要：
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.
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