Short-period Variables in TESS Full-frame Image Light Curves Identified via Convolutional Neural Networks

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dc.contributor.authorOlmschenk, Greg
dc.contributor.authorBarry, Richard K.
dc.contributor.authorSilva, Stela Ishitani
dc.contributor.authorSchnittman, Jeremy D.
dc.contributor.authorCieplak, Agnieszka M.
dc.contributor.authorPowell, Brian P.
dc.contributor.authorKruse, Ethan
dc.contributor.authorBarclay, Thomas
dc.contributor.authorSolanki, Siddhant
dc.contributor.authorOrtega, Bianca
dc.contributor.authorBaker, John
dc.contributor.authorSalinas, Mamani Yesenia Helem
dc.date.accessioned2025-01-20T16:12:48Z
dc.date.available2025-01-20T16:12:48Z
dc.date.issued2024
dc.description.abstractThe Transiting Exoplanet Survey Satellite (TESS) mission measured light from stars in similar to 85% of the sky throughout its 2 yr primary mission, resulting in millions of TESS 30-minute-cadence light curves to analyze in the search for transiting exoplanets. To search this vast data set, we aim to provide an approach that is computationally efficient, produces accurate predictions, and minimizes the required human search effort. We present a convolutional neural network that we train to identify short-period variables. To make a prediction for a given light curve, our network requires no prior target parameters identified using other methods. Our network performs inference on a TESS 30-minute-cadence light curve in similar to 5 ms on a single GPU, enabling large-scale archival searches. We present a collection of 14,156 short-period variables identified by our network. The majority of our identified variables fall into two prominent populations, one of close-orbit main-sequence binaries and another of delta Scuti stars. Our neural network model and related code are additionally provided as open-source code for public use and extension.
dc.fuente.origenWOS
dc.identifier.doi10.3847/1538-3881/ad55f1
dc.identifier.eissn1538-3881
dc.identifier.issn0004-6256
dc.identifier.urihttps://doi.org/10.3847/1538-3881/ad55f1
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/90355
dc.identifier.wosidWOS:001272148400001
dc.issue.numero2
dc.language.isoen
dc.revistaAstronomical journal
dc.rightsacceso restringido
dc.subject.ods13 Climate Action
dc.subject.odspa13 Acción por el clima
dc.titleShort-period Variables in TESS Full-frame Image Light Curves Identified via Convolutional Neural Networks
dc.typeartículo
dc.volumen168
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
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