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

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dc.contributor.authorSalinas, Helem
dc.contributor.authorPichara, Karim
dc.contributor.authorBrahm, Rafael
dc.contributor.authorPerez-Galarce, Francisco
dc.contributor.authorMery, Domingo
dc.date.accessioned2025-01-20T20:11:16Z
dc.date.available2025-01-20T20:11:16Z
dc.date.issued2023
dc.description.abstractCurrent 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.
dc.fuente.origenWOS
dc.identifier.doi10.1093/mnras/stad1173
dc.identifier.eissn1365-2966
dc.identifier.issn0035-8711
dc.identifier.urihttps://doi.org/10.1093/mnras/stad1173
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/92133
dc.identifier.wosidWOS:000981447300001
dc.issue.numero3
dc.language.isoen
dc.pagina.final3216
dc.pagina.inicio3201
dc.revistaMonthly notices of the royal astronomical society
dc.rightsacceso restringido
dc.subjectmethods: data analysis
dc.subjectplanets and satellites: detection
dc.subject.ods13 Climate Action
dc.subject.odspa13 Acción por el clima
dc.titleDistinguishing a planetary transit from false positives: a Transformer-based classification for planetary transit signals
dc.typeartículo
dc.volumen522
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
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