NASA's Kepler Space Telescope has been instrumental in the task of finding the presence of exoplanets in our galaxy. This search has been supported by computational data analysis to identify exoplanets from the signals received by the Kepler telescope. In this paper, we consider building upon some existing work on exoplanet identification using residual networks for the data of the Kepler space telescope and its extended mission K2. This paper aims to explore how deep learning algorithms can help in classifying the presence of exoplanets with less amount of data in one case and a more extensive variety of data in another. In addition to the standard CNN-based method, we propose a Siamese architecture that is particularly useful in addressing classification in a low-data scenario. The CNN and ResNet algorithms achieved an average accuracy of 68% for three classes and 86% for two-class classification. However, for both the three and two classes, the Siamese algorithm achieved 99% accuracy.

翻译：NASA的开普勒太空望远镜在发现银河系中系外行星的存在方面发挥了关键作用。这一搜索过程得到了计算数据分析的支持，以从开普勒望远镜接收到的信号中识别系外行星。本文旨在基于已有研究成果，利用残差网络对开普勒太空望远镜及其延伸任务K2的数据进行系外行星识别。本文旨在探索深度学习算法如何帮助在数据量较少和数据类型更丰富的两种情况下对系外行星的存在进行分类。除标准卷积神经网络方法外，我们提出了一种特别适用于低数据场景分类的孪生网络架构。CNN和ResNet算法在三类分类任务中达到了68%的平均准确率，在二分类任务中为86%。然而，无论是三类还是二类分类任务，孪生算法均实现了99%的准确率。