Improving the performance of weak supervision searches using data augmentation
This addresses a practical limitation in weak supervision for physics applications, though it appears incremental as it applies existing augmentation techniques to a specific domain.
The paper tackles the problem of weak supervision requiring excessive signal data by applying physics-inspired data augmentation methods like pT smearing and jet rotation, showing that this significantly enhances performance and enables efficient learning with substantially less data.
Weak supervision combines the advantages of training on real data with the ability to exploit signal properties. However, training a neural network using weak supervision often requires an excessive amount of signal data, which severely limits its practical applicability. In this study, we propose addressing this limitation through data augmentation, increasing the training data's size and diversity. Specifically, we focus on physics-inspired data augmentation methods, such as $p_{\text{T}}$ smearing and jet rotation. Our results demonstrate that data augmentation can significantly enhance the performance of weak supervision, enabling neural networks to learn efficiently from substantially less data.