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，其核心在于检测由不可见的亚恒星伴星引起的恒星速度变化。仪器误差、不规则的时间采样以及源自恒星固有变率的多种噪声源会干扰数据解释，甚至导致虚假探测。近年来，在系外行星领域开始出现使用机器学习算法的研究工作，其中一些成果超越了该领域传统技术所达到的水平。我们旨在探索神经网络在径向速度法中的应用范围，特别是在存在恒星起源相关噪声的情况下进行系外行星探测。本研究提出了一种神经网络，用于替代径向速度法中对检测信号显著性的计算，并将其分类为行星起源或非行星起源。该算法使用包含有行星伴星和无行星伴星的系统的合成数据进行训练。我们基于先前对恒星活动行为的研究，在模拟中注入了逼真的相关噪声。将该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。该网络实现的假阳性率降低了28%。这一改进主要体现于对与小质量行星相关的低振幅信号的检测中。此外，其执行时间比传统方法快了五个数量级。该算法展现出的优越性能迄今为止仅在模拟径向速度数据上进行了测试。尽管原则上将其适配用于实时时间序列应该很直接，但其性能仍需经过全面测试。未来的工作应可评估其作为系外行星探测有价值工具的潜力。