  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.
We show that in the presence of systematic uncertainties on the background
rate, our adversarially-trained, decorrelated tagger considerably outperforms a
conventionally trained neural network, despite having a slightly worse
signal-background separation power. We generalize the adversarial training
technique to include a parametric dependence on the signal hypothesis, training
a single network that provides optimized, interpolatable decorrelated jet
tagging across a continuous range of hypothetical resonance masses, after
training on discrete choices of the signal mass.
