  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.
