Decorrelated Jet Substructure Tagging using Adversarial Neural Networks

Understand

We describe a strategy for constructing a neural network jet substructure tagger which powerfully discriminates boosted decay signals while remaining largely uncorrelated with the jet mass.

• This reduces the impact of systematic uncertainties in background modeling while enhancing signal purity, resulting in improved discovery significance relative to existing taggers.
• The network is trained using an adversarial strategy, resulting in a tagger that learns to balance classification accuracy with decorrelation.
• As a benchmark scenario, we consider the case where large-radius jets originating from a boosted resonance decay are discriminated from a background of nonresonant quark and gluon jets.