Spatial missions such as the Kepler mission, and the Transiting Exoplanet Survey Satellite (TESS) mission, have encouraged data scientists to analyze light curve datasets. The purpose of analyzing these data is to look for planet transits, with the aim of discovering and validating exoplanets, which are planets found outside our Solar System. Furthermore, transiting exoplanets can be better characterized when light curves and radial velocity curves are available. The manual examination of these datasets is a task that requires big quantities of time and effort, and therefore is prone to errors. As a result, the application of machine learning methods has become more common on exoplanet discovery and categorization research. This survey presents an analysis on different exoplanet transit discovery algorithms based on machine learning, some of which even found new exoplanets. The analysis of these algorithms is divided into four steps, namely light curve preprocessing, possible exoplanet signal detection, and identification of the detected signal to decide whether it belongs to an exoplanet or not. We propose a model to create synthetic datasets of light curves, and we compare the performance of several machine learning models used to identify transit exoplanets, with inputs preprocessed with and without using the Discrete Wavelet Transform (DWT). Our experimental results allow us to conclude that multiresolution analysis in the time-frequency domain can improve exoplanet signal identification, because of the characteristics of light curves and transiting exoplanet signals.

中文翻译：

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使用机器学习技术进行系外行星发现的调查

空间任务，例如开普勒任务和过渡系外行星调查卫星（TESS）任务，鼓励数据科学家分析光曲线数据集。分析这些数据的目的是寻找行星过境，目的是发现和验证系外行星，系外行星是我们太阳系之外发现的行星。此外，当有光曲线和径向速度曲线可用时，可以更好地表征过渡系外行星。手动检查这些数据集是一项需要大量时间和精力的任务，因此容易出错。结果，机器学习方法的应用在系外行星发现和分类研究中变得越来越普遍。这项调查提出了基于机器学习的不同系外行星过境发现算法的分析，其中一些甚至发现了新的系外行星。这些算法的分析分为四个步骤，即光曲线预处理，可能的系外行星信号检测以及识别检测到的信号以决定其是否属于系外行星。我们提出了一个模型来创建光曲线的合成数据集，并比较了用于识别过流系外行星的几种机器学习模型的性能，以及使用和不使用离散小波变换（DWT）。我们的实验结果使我们得出结论，由于光曲线和瞬变系外行星信号的特征，在时频域中进行多分辨率分析可以改善系外行星信号的识别。