Despite extensive theoretical motivation for physics beyond the Standard
Model (BSM) of particle physics, searches at the Large Hadron Collider (LHC)
have found no significant evidence for BSM physics. Therefore, it is essential
to broaden the sensitivity of the search program to include unexpected
scenarios. We present a new model-agnostic anomaly detection technique that
naturally benefits from modern machine learning algorithms. The only
requirement on the signal for this new procedure is that it is localized in at
least one known direction in phase space. Any other directions of phase space
that are uncorrelated with the localized one can be used to search for
unexpected features. This new method is applied to the dijet resonance search
to show that it can turn a modest 2 sigma excess into a 7 sigma excess for a
model with an intermediate BSM particle that is not currently targeted by a
dedicated search.

介绍一种新的不受模型限制的异常检测技术，利用现代机器学习算法，应用于双喷注谐振搜索，将仅有的 2sigma 超出量变为 7sigma 超出量，从而找到与现有物理模型不符的中间 BSM 粒子。