The search for exoplanets has evolved from case by case data inspection to automatic pattern recognition methods for processing a very large number of light curves. For this reason, the use of machine learning techniques has become a common practice in the field, where deep learning models are now in the spotlight as a promising leap forward towards automation. However, despite being faster than manual inspection, they usually still need hand-crafted features to achieve good results. Moreover, not all methods allow real world data where a large portion of the data is missing or at least is not regularly sampled. In this paper, we propose a method that only requires the raw light curve to identify exoplanets without the need of additional metadata or specific formats for the time series. We transform unevenly-sampled time series (light curves) of variable length into a 2-channel fixed-sized image using Markov Transition Field, which feeds a convolutional neural network that classifies candidate transients. We conducted experiments using the Kepler Mission dataset, identifying two key results: (1) the method is competitive in terms of performance to the state-of-the-art alternatives, yet it is simpler and faster. (2) A Markov Transition Field can be used as an effective stand-alone data product for analyzing unevenly-sampled transient light curves.

对系外行星的搜索已从逐案检查变成了用于处理大量光曲线的自动模式识别方法。因此，机器学习技术的使用已成为该领域的一种普遍做法，深度学习模型如今已成为人们关注的一个朝着自动化迈进的有希望的飞跃。但是，尽管比手动检查要快，但它们通常仍需要手工制作的功能才能获得良好的结果。此外，并非所有方法都允许在现实世界中丢失大量数据或至少不定期对其进行采样的数据。在本文中，我们提出了一种仅需要原始光曲线来识别系外行星的方法，而无需为时间序列添加其他元数据或特定格式。我们使用Markov Transition Field将可变长度的不均匀采样的时间序列（光曲线）转换为2通道固定大小的图像，该图像提供了对候选瞬变进行分类的卷积神经网络。我们使用开普勒任务数据集，确定了两个关键结果：（1）该方法在性能上与最新的替代方法相比具有竞争优势，但更简单，更快捷。（2）马尔可夫转换场可以用作分析不均匀采样的瞬态光曲线的有效独立数据产品。