The detection of exoplanets with the radial velocity method consists in detecting variations of the stellar velocity caused by an unseen sub-stellar companion. Instrumental errors, irregular time sampling, and different noise sources originating in the intrinsic variability of the star can hinder the interpretation of the data, and even lead to spurious detections. In recent times, work began to emerge in the field of extrasolar planets that use Machine Learning algorithms, some with results that exceed those obtained with the traditional techniques in the field. We seek to explore the scope of the neural networks in the radial velocity method, in particular for exoplanet detection in the presence of correlated noise of stellar origin. In this work, a neural network is proposed to replace the computation of the significance of the signal detected with the radial velocity method and to classify it as of planetary origin or not. The algorithm is trained using synthetic data of systems with and without planetary companions. We injected realistic correlated noise in the simulations, based on previous studies of the behaviour of stellar activity. The performance of the network is compared to the traditional method based on null hypothesis significance testing. The network achieves 28 % fewer false positives. The improvement is observed mainly in the detection of small-amplitude signals associated with low-mass planets. In addition, its execution time is five orders of magnitude faster than the traditional method. The superior performance exhibited by the algorithm has only been tested on simulated radial velocity data so far. Although in principle it should be straightforward to adapt it for use in real time series, its performance has to be tested thoroughly. Future work should permit evaluating its potential for adoption as a valuable tool for exoplanet detection.

翻译：利用径向速度法探测系外行星，本质上是探测由不可见的亚恒星伴星引起的恒星速度变化。仪器误差、不规则时间采样以及恒星自身变异性产生的不同噪声源，会阻碍对数据的解读，甚至导致错误探测。近年来，在系外行星领域开始出现利用机器学习算法的研究，其中一些成果超越了传统技术的表现。本研究旨在探索神经网络在径向速度法中的应用范围，特别是在存在恒星相关噪声的情况下进行系外行星探测。本文提出了一种神经网络，用于替代径向速度法中对探测信号显著性的计算，并判断该信号是否源于行星。该算法使用包含与不包含行星伴星的系统模拟数据进行训练。我们基于先前对恒星活动行为的研究，在模拟中注入了真实的相关噪声。将网络的性能与基于零假设显著性检验的传统方法进行了比较。该网络的误报率降低了28%。这种改进主要出现在与小质量行星相关的低振幅信号的探测中。此外，其执行时间比传统方法快五个数量级。该算法表现出的优越性能目前仅在模拟径向速度数据上进行了测试。尽管原则上应能直接适应于实时序列的应用，但其性能仍需经过彻底测试。未来的工作应能评估其作为系外行星探测有价值工具的潜力。