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

TL;DR

This paper explores the importance of features in deep learning for identifying new physics signals at the LHC, introduces a method to construct more effective features, and demonstrates improved signal-background separation.

Contribution

It presents a novel deep learning-based approach to construct features that enhance physics searches and assesses feature importance in BSM scenarios.

Findings

Additional features can increase discovery reach in multi-Higgs scenarios.

Constructed features outperform traditional features in separating signals from backgrounds.

The approach can detect momentum biases in detectors.

Abstract

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.