A novel multi-variate analysis method for searching particles in high energy physics

In order to search channel of Higgs bosons in high energy physics (HEP), in this paper we propose Deep Extreme Feature Extraction (DEFE), a new ensemble multi-variate Analysis method (MVA). DEFE is a deep ensemble learning scheme that trains a strongly diverse set of neural feature learners without explicitly encouraging diversity and penalizing correlations, which is achieved by adopting an implicit neural controller that directly controls and distributes gradient flows from higher level deep prediction network. Based on the construction and approximation of the extreme selection region presented in this paper, the DEFE model is able to be trained efficiently, and extract discriminative features from multiple angles and dimensions, hence the improvement of the selection region of searching new particles in HEP can be achieved. With the application in this model, the selection regions full of signal process can be obtained through the training of a miniature collision events set. In comparison of the classic deep neural network (DNN), DEFE shows a state-of-the-art performance: the error rate has decreased by about 37%, the accuracy has broken through 90% for the first time along with the discovery significance which has reached a standard deviation of 6.0. Experiment shows that DEFE is able to train an ensemble of discriminative feature learners that boosts the over-performance of final prediction. Furthermore, among high-level features, there are still some important patterns that are unidentified by DNN and are independent from low-level features, while DEFE can identify these significant patterns more effectively and efficiently.