Tag N' Train: A Technique to Train Improved Classifiers on Unlabeled Data

Understand

There has been substantial progress in applying machine learning techniques to classification problems in collider and jet physics.

• But as these techniques grow in sophistication, they are becoming more sensitive to subtle features of jets that may not be well modeled in simulation.
• Therefore, relying on simulations for training will lead to sub-optimal performance in data, but the lack of true class labels makes it difficult to train on real data.
• To address this challenge we introduce a new approach, called Tag N' Train (TNT), that can be applied to unlabeled data that has two distinct sub-objects.