In this paper, we propose Deep Extreme Feature Extraction (DEFE), a new
ensemble MVA method for searching ?+?- channel of Higgs bosons in high
energy physics. DEFE can be viewed as 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 (not involved in feed forward
computation) that directly controls and distributes gradient flows from
higher level deep prediction network. Such model-independent controller
results in that every single local feature learned are used in the
feature-to-output mapping stage, avoiding the blind averaging of features.
DEFE makes the ensembles ?deep? in the sense that it allows deep
post-process of these features that try to learn to select and abstract the
ensemble of neural feature learners. Based the construction and
approximation of the so-called extreme selection region, 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 of this model, a selection region full of signal processes can
be obtained through the training of miniature collision events set. In
comparison with the Classic Deep Neural Network, 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 has reached a standard deviation of 6.0?. Experimental data
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 of low-level features, while DEFE is
able to identify these significant patterns more efficiently
Michel Borghini as a Mentor and Father of the Theory of Polarization in Polarized Targets