Machine learning (ML) is becoming a critical tool for interrogation of large
complex data. Labeling, defined as the process of adding meaningful
annotations, is a crucial step of supervised ML. However, labeling datasets is
time consuming. Here we show that convolutional neural networks (CNNs), trained
on crudely labeled astronomical videos, can be leveraged to improve the quality
of data labeling and reduce the need for human intervention. We use videos of
the solar magnetic field, crudely labeled into two classes: emergence or
non-emergence of bipolar magnetic regions (BMRs), based on their first
detection on the solar disk. We train CNNs using crude labels, manually verify,
correct labeling vs. CNN disagreements, and repeat this process until
convergence. Traditionally, flux emergence labelling is done manually. We find
that a high-quality labeled dataset, derived through this iterative process,
reduces the necessary manual verification by 50%. Furthermore, by gradually
masking the videos and looking for maximum change in CNN inference, we locate
BMR emergence time without retraining the CNN. This demonstrates the
versatility of CNNs for simplifying the challenging task of labeling complex
dynamic events.

机器学习（ML）作为处理大规模复杂数据的关键工具，标注（labeling）是监督式 ML 的重要步骤。本文使用卷积神经网络（CNNs）对天体视频进行训练，以改善数据标注质量并减少人工干预。研究发现通过这种迭代过程获得的高质量标注数据集可以减少 50% 的人工验证，并且通过渐进遮盖视频并检测 CNN 推断的最大变化，可以定位双极磁区（BMRs）的出现时间，展示了 CNNs 在简化复杂动态事件标注任务中的多功能性。