标题： 将物理先验知识融入弱监督异常检测 显示英文标题

标题： Incorporating Physical Priors into Weakly-Supervised Anomaly Detection

摘要： 我们提出了一种新的基于机器学习的异常检测策略，用于将数据与仅背景参考进行比较（这是一种弱监督的形式）。当信号过于罕见或存在许多无用特征时，先前策略的灵敏度会显著下降。我们的先验辅助弱监督（PAWS）方法结合了一类信号模型的信息，显著增强了弱监督方法的搜索灵敏度。只要真实信号在预设的类别中，PAWS就能在不提前指定确切参数的情况下，达到专用的全监督方法的灵敏度。在LHC奥运会异常检测基准数据集上，我们的半监督和弱监督学习组合能够将灵敏度比先前方法提高一个数量级。此外，如果我们向输入中添加无关（噪声）维度，经典方法的灵敏度会再降低一个数量级，而PAWS对噪声不敏感。这种新方法可以应用于多种场景，并推动了完全模型不可知方法和完全模型特定搜索之间的灵敏度前沿。 显示英文摘要

摘要： We propose a new machine-learning-based anomaly detection strategy for comparing data with a background-only reference (a form of weak supervision). The sensitivity of previous strategies degrades significantly when the signal is too rare or there are many unhelpful features. Our Prior-Assisted Weak Supervision (PAWS) method incorporates information from a class of signal models to significantly enhance the search sensitivity of weakly supervised approaches. As long as the true signal is in the pre-specified class, PAWS matches the sensitivity of a dedicated, fully supervised method without specifying the exact parameters ahead of time. On the benchmark LHC Olympics anomaly detection dataset, our mix of semi-supervised and weakly supervised learning is able to extend the sensitivity over previous methods by a factor of 10 in cross section. Furthermore, if we add irrelevant (noise) dimensions to the inputs, classical methods degrade by another factor of 10 in cross section while PAWS remains insensitive to noise. This new approach could be applied in a number of scenarios and pushes the frontier of sensitivity between completely model-agnostic approaches and fully model-specific searches.