  The transit method is one of the most relevant exoplanet detection
techniques, which consists of detecting periodic eclipses in the light curves
of stars. This is not always easy due to the presence of noise in the light
curves, which is induced, for example, by the response of a telescope to
stellar flux. For this reason, we aimed to develop an artificial neural network
model that is able to detect these transits in light curves obtained from
different telescopes and surveys. We created artificial light curves with and
without transits to try to mimic those expected for the extended mission of the
Kepler telescope (K2) in order to train and validate a 1D convolutional neural
network model, which was later tested, obtaining an accuracy of 99.02 % and an
estimated error (loss function) of 0.03. These results, among others, helped to
confirm that the 1D CNN is a good choice for working with non-phased-folded
Mandel and Agol light curves with transits. It also reduces the number of light
curves that have to be visually inspected to decide if they present
transit-like signals and decreases the time needed for analyzing each (with
respect to traditional analysis).
