Mon. Not. R. Astron. Soc., 488, 5232-5250 (2019/October-1)
Classifying exoplanet candidates with convolutional neural networks: application to the Next Generation Transit Survey.
CHAUSHEV A., RAYNARD L., GOAD M.R., EIGMULLER P., ARMSTRONG D.J., BRIEGAL J.T., BURLEIGH M.R., CASEWELL S.L., GILL S., JENKINS J.S., NIELSEN L.D., WATSON C.A., WEST R.G., WHEATLEY P.J., UDRY S. and VINES J.I.
Abstract (from CDS):
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±0.2) per cent and an accuracy of (88.5±0.3) per cent on our unseen test data, as well as (76.5±0.4) per cent and (74.6±1.1) 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.
© 2019 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society
methods: data analysis - techniques: photometric - planets and satellites: detection
View the reference in ADS
To bookmark this query, right click on this link: simbad:2019MNRAS.488.5232C and select 'bookmark this link' or equivalent in the popup menu