Distinguishing a planetary transit from false positives: a Transformer-based classification for planetary transit signals

Helem Salinas, Karim Pichara, Rafael Brahm, Francisco Pérez-Galarce, Domingo Mery

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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.

Original languageEnglish
Pages (from-to)3201-3216
Number of pages16
JournalMonthly Notices of the Royal Astronomical Society
Issue number3
StatePublished - 1 Jul 2023
Externally publishedYes


  • methods: data analysis
  • planets
  • satellites: detection


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