论文标题
反QCD喷气标记的不变表示驱动神经分类器
Invariant Representation Driven Neural Classifier for Anti-QCD Jet Tagging
论文作者
论文摘要
我们利用基于神经网络的标准模型喷气分类任务中的表示和电感偏差来检测非QCD信号喷气机。在建立基于分类的喷气物理学中基于分类的异常检测框架时，我们证明，使用\ emph {良好校准}和\ emph {功能强大的功能提取器}，训练良好的\ emph {mass-dectectated}监督的标准模型Neural Jetifier可以用作强大的一般抗QCCD Quatger，以有效地改进了QC的QC。施加\ emph {data-aigmented}质量变异（从而使主要因素解耦）不仅促进了背景估计，而且还会引起更多的子结构感知表示的表示学习。我们能够达到所有考虑的测试信号的出色标记效率。在最好的情况下，我们达到51的背景排斥率，在50 \％信号接受度下，显着性提高因子为3.6，而射流质量降低了。这项研究表明，监督的标准模型喷气分类器在一般的新物理搜索中具有巨大的潜力。
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.
