论文标题
图像分类算法，用于确定ASAS-SN Eclips Binaries的光曲线形态
Image Classification Algorithm for Determining the Light Curve Morphologies of ASAS-SN Eclipsing Binaries
论文作者
论文摘要
我们介绍了ASAS-SN根据其光曲线图像观察到的光曲线形态的分类。收集了具有三个不同类别（分离的Algol类型，$β$ Lyly类型和W UMA类型）的16500个黯然失色系统的数据以构建其光曲线。图像上采用了包含卷积神经网络的深度学习算法，以实现令人满意的分类。称为AsebClass的代码是用Python语言编写的，它使用Keras API使用TensorFlow平台在模型的训练阶段使用数学库。该体系结构由四组卷积，激活，最大池层以及其他完全连接的层组成。结果表明，我们的算法估计了外部输入图像数据的形态学类别，其精度为92％。
We present a classification of the light curve morphologies of eclipsing binary systems observed by ASAS-SN based on their light curve images. The data of 16500 eclipsing systems having three different classes (detached Algol type, $β$ Lyr type, and W UMa type) are collected to construct their light curves. A deep learning algorithm containing the convolutional neural networks is employed on the images to achieve a satisfying classification. A code called ASEBCLASS is written in Python language, and it uses the TensorFlow platform through Keras API to employ the mathematical libraries in the training phase of the model. The architecture consists of four groups of convolutional, activation, maximum pooling layers together with the additional fully connected layers. The results show that our algorithm estimates the morphological class of an external input image data with an accuracy value of 92%.
