发表机构
UCLA; University of Colorado; NASA Jet Propulsion Laboratory（加州大学洛杉矶分校; 科罗拉多大学; 美国国家航空航天局喷气推进实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
PISCES是一种物理信息卷积自编码器，在无标签的OMNI太阳风数据上训练，通过分解异常分数并利用物理约束，实现L1点太阳风瞬变的早期检测与预警，可提前于急始事件。
AI 中文摘要
空间天气早期预警依赖于在太阳风瞬变到达地球之前，于第一个日地拉格朗日点（L1）的就地测量中检测到这些瞬变。固定阈值可能遗漏磁场与等离子体的联合结构，且许多学习方法仅提供单一的异常分数。我们提出了用于空间天气的物理信息太阳风卷积自编码器（PISCES），这是一种在物理约束下，无需目录标签，在OMNI太阳风测量数据上训练的卷积自编码器。其损失函数包括磁场一致性、温度与速度之间的经验关系、帕克螺旋角，以及对由重建计算得到的导出量在连续一分钟样本之间变化的惩罚。在推理时，PISCES将异常分数分解为磁场和等离子体重建误差、物理关系以及残差修正，并报告每个贡献的大小。在验证数据上选择的跳跃连接衰减，提高了已训练模型的平均精度，而未训练模型的分数几乎保持不变。已训练模型还给出了这些物理贡献更一致的排序。经过尾部中位数平滑后，警报可以提前于独立观测到的急始，包括正急始脉冲。
英文摘要
Space weather early warning depends on detecting solar wind transients in in-situ measurements at the first Sun-Earth Lagrange point (L1), before they reach Earth. Fixed thresholds can miss combined magnetic and plasma structure, and many learning methods provide a single anomaly score. We present the Physics-Informed Solar-wind Convolutional autoEncoder for Space-weather (PISCES), a convolutional autoencoder trained without catalog labels on OMNI solar wind measurements under physics constraints. Its loss includes magnetic field consistency, an empirical relation between temperature and velocity, the Parker spiral angle, and penalties on changes between consecutive one-minute samples in derived quantities calculated from the reconstruction. At inference, PISCES separates the anomaly score into magnetic and plasma reconstruction errors, physics relations, and residual corrections, and reports the magnitude of each contribution. Attenuation of the skip connections, selected on validation data, improves average precision for the trained models, while the untrained scores remain nearly the same. The trained models also give a more consistent ordering of these physical contributions. After smoothing with a trailing median, the alarms can precede independently observed sudden commencements, including positive sudden impulses.
CommentsPoster presented at NASA 5th Eddy Cross-Disciplinary Symposium, May 2026. Available at: https://github.com/magnaprog/PISCES
