Invariant Representation Driven Neural Classifier for Anti-QCD Jet Tagging
This provides a method for anomaly detection in jet physics, potentially aiding new physics searches, though it appears incremental as it builds on existing supervised classifiers with added invariance constraints.
The paper tackled the problem of detecting non-QCD signal jets in particle physics by using a mass-decorrelated supervised neural classifier, achieving a background rejection rate of 51 and a significance improvement factor of 3.6 at 50% signal acceptance.
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 Standard Model neural jet classifier can serve as a strong generic anti-QCD jet tagger for effectively reducing the QCD background. Imposing \emph{data-augmented} mass-invariance (and thus 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 of 51 and a significance improvement factor of 3.6 at 50 \% signal acceptance, with the jet mass decorrelated. This study indicates that supervised Standard Model jet classifiers have great potential in general new physics searches.