Trajectory-Agnostic Asteroid Detection in TESS with Deep Learning
Brian P. Powell, Jorge Martinez-Palomera, Amy Tuson, Christina Hedges, Jessie Dotson + 1 more
TLDR
This paper introduces a deep learning W-Net method for trajectory-agnostic asteroid detection in TESS data, robust to varying speeds and directions.
Key contributions
- Introduces a W-Net (stacked 3D U-Nets) to detect moving objects in TESS image time-series data.
- Achieves trajectory-agnostic asteroid detection using data augmentation, robust to speed/direction.
- Develops Adaptive Normalization, a novel learned data scaling method for optimal processing.
- Releases `tess-asteroid-ml` code for creating TESS asteroid training data for the community.
Why it matters
This paper offers a significant leap in asteroid detection by removing trajectory assumptions, a common hurdle in traditional methods. Its deep learning approach and novel scaling are highly adaptable, promising enhanced discovery in current and future astronomical surveys like Roman and NEOSurveyor.
Original Abstract
We present a novel method for extracting moving objects from TESS data using machine learning. Our approach uses two stacked 3D U-Nets with skip connections, which we call a W-Net, to filter background and identify pixels containing moving objects in TESS image time-series data. By augmenting the training data through rotation of the image cubes, our method is robust to differences in speed and direction of asteroids, requiring no assumptions for either parameter range which are typically required in "shift-and-stack" type algorithms. We also developed a novel method for learned data scaling that we call Adaptive Normalization, which allows the neural network to learn the ideal range and scaling distribution required for optimal data processing. We built a code for creating TESS training data with asteroid masks that served as the foundation of our effort (tess-asteroid-ml), which we publicly released for the benefit of the community. Our method is not limited to TESS, but applicable for implementation in other similar time-domain surveys, making it of particular interest for use with data from upcoming missions such as the Nancy Grace Roman Space Telescope and NEOSurveyor.
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