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 jet mass decorrelated. This study indicates that supervised Standard Model jet classifiers have great potential in general new physics searches.

翻译：我们利用基于神经网络的标准模型喷气式喷气机分类任务的代表性学习和感应偏差,以探测非QCD信号喷射机。在建立基于分类的喷气物理异常探测框架时,我们证明,如果采用\ emph{ well-calibraced} 和\ emph{ powerful 足够的地貌提取机},经过良好训练的\ emph{mas- decor contor} 标准神经模型喷气机分类器可以作为强大的通用反QCD喷气式喷气式追踪器,有效减少QCD背景。实施 实施 emph{ data-subed} 质量变化(从而分离主导因素) 不仅有利于背景估计,而且能引发更多的次结构化代表学习。 我们能为所考虑的所有测试信号达到极佳的标记效率。 在最好的情况下,我们达到51个背景拒绝率,在50 ⁇ 信号接收时达到3.6 的重大改进系数,与喷气质量有关。这项研究表明, 受监督的标准模型喷气式喷射式分类器分类器在一般新的物理学搜索中有很大潜力。