论文标题
与密度估计的异常检测
Anomaly Detection with Density Estimation
论文作者
论文摘要
我们利用最近在神经密度估计的突破来提出一种新的无监督异常检测技术（阳极）。通过估计信号区域和边带中数据的概率密度，并将后者插入信号区域，可以构建数据与背景的似然比。这种可能性比对可能由于局部异常引起的数据过度敏感。此外，阳极方法的独特潜在优势是可以使用学习的密度直接估算背景。最后，阳极对信号区域和边带之间的系统差异具有鲁棒性，比其他方法更广泛地适用。我们使用LHC奥运会2020 R \＆D数据集证明了这种新方法的力量。我们展示了Anode如何在背景预测上以10 \％的精度增强7倍的Dijet Bump狩猎的意义。尽管LHC被用作反复出现的例子，但此处开发的方法对物理及其他地区的异常检测具有更广泛的适用性。
We leverage recent breakthroughs in neural density estimation to propose a new unsupervised anomaly detection technique (ANODE). By estimating the probability density of the data in a signal region and in sidebands, and interpolating the latter into the signal region, a likelihood ratio of data vs. background can be constructed. This likelihood ratio is broadly sensitive to overdensities in the data that could be due to localized anomalies. In addition, a unique potential benefit of the ANODE method is that the background can be directly estimated using the learned densities. Finally, ANODE is robust against systematic differences between signal region and sidebands, giving it broader applicability than other methods. We demonstrate the power of this new approach using the LHC Olympics 2020 R\&D Dataset. We show how ANODE can enhance the significance of a dijet bump hunt by up to a factor of 7 with a 10\% accuracy on the background prediction. While the LHC is used as the recurring example, the methods developed here have a much broader applicability to anomaly detection in physics and beyond.
