论文标题
具有多输入神经网络的可变星分类
Variable star classification with a Multiple-Input Neural Network
论文作者
论文摘要
在本实验中，我们创建了一个多输入神经网络，该网络由卷积和多层神经网络组成。使用此设置，选定的表现最高的神经网络能够根据其光曲线的视觉特征来区分可变星，同时还考虑了其他数值信息（例如，时期，无红亮度），以区分视觉上相似的光曲线。使用所有OGLE-III观察场，相折叠的光曲线和周期数据对网络进行了训练和测试。对于大多数主要类别的神经网络（$δ$ scutis，eclips binaries，rr lyrae stars，type-ii cepheids）的神经网络的精度为89-99 \％，只有第一个过度的异常cepheids的准确度为45 \％。为了抵消第一过反激异常的头孢虫和rrab星之间的巨大混淆，我们添加了无红亮度作为新输入，只保留了来自LMC场的恒星以具有固定距离。随着这一变化，我们提高了神经网络的首次反向异常头孢虫的结果近80 \％。总体而言，我们团队开发的多输入神经网络方法是现有分类方法的有前途的替代方法。
In this experiment, we created a Multiple-Input Neural Network, consisting of Convolutional and Multi-layer Neural Networks. With this setup the selected highest-performing neural network was able to distinguish variable stars based on the visual characteristics of their light curves, while taking also into account additional numerical information (e.g. period, reddening-free brightness) to differentiate visually similar light curves. The network was trained and tested on OGLE-III data using all OGLE-III observation fields, phase-folded light curves and period data. The neural network yielded accuracies of 89--99\% for most of the main classes (Cepheids, $δ$ Scutis, eclipsing binaries, RR Lyrae stars, Type-II Cepheids), only the first-overtone Anomalous Cepheids had an accuracy of 45\%. To counteract the large confusion between the first-overtone Anomalous Cepheids and the RRab stars we added the reddening-free brightness as a new input and only stars from the LMC field were retained to have a fixed distance. With this change we improved the neural network's result for the first-overtone Anomalous Cepheids to almost 80\%. Overall, the Multiple-input Neural Network method developed by our team is a promising alternative to existing classification methods.
