基于密度估计的异常检测
Anomaly Detection with Density Estimation
摘要：
我们利用神经密度估计的最新突破，提出了一种新的无监督异常检测技术（ANODE）。通过估计信号区域和边带中的数据的概率密度，并将后者内插到信号区域中，可以构造数据与背景的似然比。该似然比对可能由于局部异常而导致的数据中的过密度广泛敏感。此外，ANODE方法的一个独特的潜在好处是可以使用学习的密度直接估计背景。最后，ANODE对信号区域和边带之间的系统差异具有鲁棒性，使其比其他方法具有更广泛的适用性。我们使用LHC Olympics 2020 R\&D Dataset展示了这种新方法的强大功能。我们展示了如何ANODE可以提高一个dijet颠簸狩猎的意义高达7倍，10%的准确度的背景预测。虽然LHC被用作反复出现的例子，但这里开发的方法对物理学及其他领域的异常检测具有更广泛的适用性。
英文摘要：
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.
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