Details

Autor(en) / Beteiligte

• Chaushev, Alexander
• Raynard, Liam
• Goad, Michael R
• Eigmüller, Philipp
• Armstrong, David J
• Briegal, Joshua T
• Burleigh, Matthew R
• Casewell, Sarah L
• Gill, Samuel
• Jenkins, James S
• Nielsen, Louise D
• Watson, Christopher A
• West, Richard G
• Wheatley, Peter J
• Udry, Stéphane
• Vines, Jose I

Titel

Classifying exoplanet candidates with convolutional neural networks: application to the Next Generation Transit Survey

Ist Teil von

• Monthly notices of the Royal Astronomical Society, 2019-10, Vol.488 (4), p.5232-5250

Ort / Verlag

Oxford University Press

Erscheinungsjahr

2019

Link zum Volltext

Quelle

EZB Free E-Journals

Beschreibungen/Notizen

• ABSTRACT Vetting of exoplanet candidates in transit surveys is a manual process, which suffers from a large number of false positives and a lack of consistency. Previous work has shown that convolutional neural networks (CNN) provide an efficient solution to these problems. Here, we apply a CNN to classify planet candidates from the Next Generation Transit Survey (NGTS). For training data sets we compare both real data with injected planetary transits and fully simulated data, as well as how their different compositions affect network performance. We show that fewer hand labelled light curves can be utilized, while still achieving competitive results. With our best model, we achieve an area under the curve (AUC) score of $(95.6\pm {0.2}){{\ \rm per\ cent}}$ and an accuracy of $(88.5\pm {0.3}){{\ \rm per\ cent}}$ on our unseen test data, as well as $(76.5\pm {0.4}){{\ \rm per\ cent}}$ and $(74.6\pm {1.1}){{\ \rm per\ cent}}$ in comparison to our existing manual classifications. The neural network recovers 13 out of 14 confirmed planets observed by NGTS, with high probability. We use simulated data to show that the overall network performance is resilient to mislabelling of the training data set, a problem that might arise due to unidentified, low signal-to-noise transits. Using a CNN, the time required for vetting can be reduced by half, while still recovering the vast majority of manually flagged candidates. In addition, we identify many new candidates with high probabilities which were not flagged by human vetters.