  Current space-based missions, such as the Transiting Exoplanet Survey
Satellite (TESS), provide a large database of light curves that must be
analysed efficiently and systematically. In recent years, deep learning (DL)
methods, particularly convolutional neural networks (CNN), have been used to
classify transit signals of candidate exoplanets automatically. However, CNNs
have some drawbacks; for example, they require many layers to capture
dependencies on sequential data, such as light curves, making the network so
large that it eventually becomes impractical. The self-attention mechanism is a
DL technique that attempts to mimic the action of selectively focusing on some
relevant things while ignoring others. Models, such as the Transformer
architecture, were recently proposed for sequential data with successful
results. Based on these successful models, we present a new architecture for
the automatic classification of transit signals. Our proposed architecture is
designed to capture the most significant features of a transit signal and
stellar parameters through the self-attention mechanism. In addition to model
prediction, we take advantage of attention map inspection, obtaining a more
interpretable DL approach. Thus, we can identify the relevance of each element
to differentiate a transit signal from false positives, simplifying the manual
examination of candidates. We show that our architecture achieves competitive
results concerning the CNNs applied for recognizing exoplanetary transit
signals in data from the TESS telescope. Based on these results, we demonstrate
that applying this state-of-the-art DL model to light curves can be a powerful
technique for transit signal detection while offering a level of
interpretability.
