Domain adaptation with gradient reversal for MC/real data calibration

Abstract

It is quite common part of the data analysis in High Energy Physics to train a classifier for signal and background separation. In case the signal under investigation is a rare process, the signal sample is simulated and background sample is taken from the real data. Such setting create an unnecessary bias: the classifier might learn not the characteristic of the signal but the characteristic of the imperfect simulation. So the challenge is to train the classifier in such way that it picks up signal/background difference and doesnt overfit to the simulation-specific features. The suggested approach is based on cross-domain adaptation technique using neural networks with gradient reversal. The network architecture is a dense multi-branch structure. One branch is responsible for the signal/background discrimination, the second branch helps to avoid the overfitting on the Monte-Carlo training dataset. The tests showed that this architecture is a robust mechanism for choosing trade-offs between discrimination power and overfitting. So the resulting networks successfully distinguishes the signal from the background, but does not distinguish simulated events from the real ones. Moreover, such architecture could to be easily extended with more branches, and each one could be responsible for specific discrete and continuous domains. For example, the additional third network’s branch could help to reduce the correlation between the classifier predictions and reconstructed mass of the decay, thereby making such approach highly viable for wide variety of physics searches. But such network’s extensions weren’t investigated during this work.