Rapid classification of TESS planet candidates with convolutional neural networks

Osborn, H. P.; Ansdell, M.; Ioannou, Y.; Sasdelli, M.; Angerhausen, D.; Caldwell, D.; Jenkins, J. M.; Räissi, C.; Smith, J. C. (2020). Rapid classification of TESS planet candidates with convolutional neural networks. Astronomy and astrophysics, 633, A53. EDP Sciences 10.1051/0004-6361/201935345

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Accurately and rapidly classifying exoplanet candidates from transit surveys is a goal of growing importance as the data rates from space-based survey missions increases. This is especially true for NASA's TESS mission which generates thousands of new candidates each month. Here we created the first deep learning model capable of classifying TESS planet candidates. We adapted the neural network model of Ansdell et al. (2018) to TESS data. We then trained and tested this updated model on 4 sectors of high-fidelity, pixel-level simulations data created using the Lilith simulator and processed using the full TESS SPOC pipeline. We find our model performs very well on our simulated data, with 97% average precision and 92% accuracy on planets in the 2-class model. This accuracy is also boosted by another ~4% if planets found at the wrong periods are included. We also performed 3- and 4-class classification of planets, blended & target eclipsing binaries, and non-astrophysical false positives, which have slightly lower average precision and planet accuracies, but are useful for follow-up decisions. When applied to real TESS data, 61% of TCEs coincident with currently published TOIs are recovered as planets, 4% more are suggested to be EBs, and we propose a further 200 TCEs as planet candidates.

Item Type:

Journal Article (Original Article)

Division/Institute:

08 Faculty of Science > Physics Institute > Space Research and Planetary Sciences
08 Faculty of Science > Physics Institute
10 Strategic Research Centers > Center for Space and Habitability (CSH)
08 Faculty of Science > Physics Institute > NCCR PlanetS

UniBE Contributor:

Angerhausen, Daniel

Subjects:

500 Science > 520 Astronomy
500 Science > 530 Physics

ISSN:

0004-6361

Publisher:

EDP Sciences

Language:

English

Submitter:

Danielle Zemp

Date Deposited:

05 May 2020 10:38

Last Modified:

10 May 2020 02:44

Publisher DOI:

10.1051/0004-6361/201935345

BORIS DOI:

10.7892/boris.142622

URI:

https://boris.unibe.ch/id/eprint/142622

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