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.

使用神经密度估计的新型无监督异常检测技术 ANODE，在区分信号区域和旁带处估计概率密度，并将旁带差值插值到信号区域，构建数据对背景的似然比，从而对局部异常的过密度进行广泛敏感。在 LHC 2020 奥林匹克研发数据集上进行实验证明，相对于其他方法，ANODE 对信号区域和旁带之间的系统差异具有更广泛的适用性，可以将 dijet 波峰探测的显著性提高多达 7 倍，在背景预测准确率为 10％时。虽然 LHC 被用作反复的示例，但此处开发的方法在物理和其他领域的异常检测中具有广泛的适用性。