  Advances in machine learning have led to an emergence of new paradigms in the
analysis of large data which could assist traditional approaches in the search
for new physics amongst the immense Standard Model backgrounds at the Large
Hadron Collider. Deep learning is one such paradigm. In this work, we first
study feature importance ranking of signal-background classification features
with deep learning for two Beyond Standard Model benchmark cases: a multi-Higgs
and a supersymmetry scenario. We find that the discovery reach for the
multi-Higgs scenario could still increase with additional features. In
addition, we also present a deep learning-based approach to construct new
features to separate signals from backgrounds using the ATLAS detector as a
specific example. We show that the constructed feature is more effective in
signal-background separation than commonly used features, and thus is better
for physics searches in the detector. As a side application, the constructed
feature may be used to identify any momentum bias in a detector. We also
utilize a convolutional neural network as part of the momentum bias checking
approach.
