# NotPlaNET: Removing False Positives from Planet Hunters TESS with Machine Learning

arXiv: https://arxiv.org/abs/2405.18278 · HTML: https://mlsanity.com/paper/2405.18278/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 25 references that are in the map.
- First posted on arXiv 28 May 2024.

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Introduction to Convolutional Neural Networks](https://mlsanity.com/paper/1511.08458/) (2015)
- [Identifying Planetary Transit Candidates in TESS Full-Frame Image Light Curves via Convolutional Neural Networks](https://mlsanity.com/paper/2101.10919/) (2021)
- [Distinguishing a planetary transit from false positives: a Transformer-based classification for planetary transit signals](https://mlsanity.com/paper/2304.14283/) (2023)
- [Identifying Exoplanets with Deep Learning. V. Improved Light Curve Classification for TESS Full Frame Image Observations](https://mlsanity.com/paper/2301.01371/) (2023)
- [Discovering Long-period Exoplanets using Deep Learning with Citizen Science Labels](https://mlsanity.com/paper/2211.06903/) (2022)

## Built on by (most cited first, 1 of 1)

- [Estimating Orbital Parameters of Direct Imaging Exoplanet Using Neural Network](https://mlsanity.com/paper/2510.17459/) (2025)

## Abstract (arXiv)

Differentiating between real transit events and false positive signals in photometric time series data is a bottleneck in the identification of transiting exoplanets, particularly long-period planets. This differentiation typically requires visual inspection of a large number of transit-like signals to rule out instrumental and astrophysical false positives that mimic planetary transit signals. We build a one-dimensional convolutional neural network (CNN) to separate eclipsing binaries and other false positives from potential planet candidates, reducing the number of light curves that require human vetting. Our CNN is trained using the TESS light curves that were identified by Planet Hunters citizen scientists as likely containing a transit. We also include the background flux and centroid information. The light curves are visually inspected and labeled by project scientists and are minimally pre-processed, with only normalization and data augmentation taking place before training. The median percentage of contaminants flagged across the test sectors is 18% with a maximum of 37% and a minimum of 10%. Our model keeps 100% of the planets for 16 of the 18 test sectors, while incorrectly flagging one planet candidate (0.3%) for one sector and two (0.6%) for the remaining sector. Our method shows potential to reduce the number of light curves requiring manual vetting by up to a third with minimal misclassification of planet candidates.