用深度学习从TESS数据中无偏检测小行星，无需预设运动参数。
Trajectory-Agnostic Asteroid Detection in TESS with Deep Learning
- 采用双3D U-Net堆叠结构（W-Net）提取时序图像中的移动目标。
- 通过图像立方体旋转增强数据，实现对速度和方向的鲁棒性。
- 提出自适应归一化方法，提升模型对数据分布的适配能力。
我们提出一种基于机器学习的新方法，从TESS时序图像数据中提取移动天体。该方法采用两个堆叠的3D U-Net并带跳跃连接，称为W-Net，用于过滤背景并识别包含移动物体的像素。通过旋转图像立方体进行数据增强，使方法对小行星的速度和方向变化具有鲁棒性，无需传统“移位叠加”算法中常见的参数范围假设。我们还开发了一种名为自适应归一化（Adaptive Normalization）的新型学习型数据缩放方法，使神经网络能自动学习最优的数据处理范围与分布。我们构建了用于生成带有小行星掩码的TESS训练数据的代码（tess-asteroid-ml），并公开发布以供社区使用。该方法不仅适用于TESS，还可推广至其他类似时域巡天项目，对即将开展的南希·格雷斯·罗曼太空望远镜和NEOSurveyor任务具有重要应用价值。
原文摘要 · Abstract (English)
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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