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Identifying Exoplanets with Deep Learning. V. Improved Light Curve Classification for TESS Full Frame Image Observations

Physics

Identifying Exoplanets with Deep Learning. V. Improved Light Curve Classification for TESS Full Frame Image Observations

E. Tey, D. Moldovan, et al.

Discover the groundbreaking Astronet-Triage-v2, a deep learning marvel developed by a talented team including Evan Tey, Dan Moldovan, and Michelle Kunimoto. This model excels in classifying TESS light curves to identify exoplanets with astonishing accuracy—99.6% recall and 75.7% precision. It's already improving our quest for new planetary candidates!

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Playback language: English
Abstract
This paper presents Astronet-Triage-v2, an improved deep learning model for classifying light curves from the TESS mission to identify exoplanets. Using a large, manually reviewed dataset of light curves, Astronet-Triage-v2 achieves 99.6% recall and 75.7% precision in identifying transiting/eclipsing events on the test set. It shows a 4% improvement in AUC-PR over its predecessor, Astronet-Triage, and recovers more planet candidates from the TESS Object of Interest (TOI) catalog. The model is currently used in the TESS Quick-Look Pipeline.
Publisher
arXiv
Published On
Jan 05, 2023
Authors
Evan Tey, Dan Moldovan, Michelle Kunimoto, Chelsea X Huang, Avi Shporer, Tansu Daylan, Daniel Muthukrishna, Andrew Vanderburg, Anne Dattilo, George R Ricker, S Seager
Tags
exoplanets
TESS
deep learning
light curves
transiting events
Astronet-Triage-v2
classification

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