Invariant Representation Driven Neural Classifier for Anti-QCD Jet Tagging

We leverage representation learning and the inductive bias in neural-net-based Standard Model jet classification tasks, to detect non-QCD signal jets. In establishing the framework for classification-based anomaly detection in jet physics, we demonstrate that with a \emph{well-calibrated} and \emph{powerful enough feature extractor}, a well-trained \emph{mass-decorrelated} supervised neural jet tagger can serve as a strong generic anti-QCD jet tagger for effectively reducing the QCD background. Imposing \emph{data-augmented} mass-invariance (decoupling the dominant factor) not only facilitates background estimation, but also induces more substructure-aware representation learning. We are able to reach excellent tagging efficiencies for all the test signals considered. In the best case, we reach a background rejection rate around 50 and a significance improvement factor of 3.6 at 50 \% signal acceptance, with jet mass decorrelated. This study indicates that supervised Standard Model jet classifiers have great potential in general new physics searches.