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

Importance and construction of features in identifying new physics signals with deep learning