论文标题
LHC喷气机中有什么异常？
What's Anomalous in LHC Jets?
论文作者
论文摘要
搜索异常是LHC的重要动机，并有助于定义关键分析步骤，包括触发器。我们讨论了如何通过概率密度估计来定义LHC异常，在物理空间或适当的神经网络潜在空间中进行评估，并讨论选择适当的数据参数化的模型依赖性。我们为经典K-均值聚类，DIRICHLET变异自动编码器和可逆神经网络进行了说明。对于来自黑暗部门的喷气机的两个特别具有挑战性的情况，我们评估了每种方法的优势和局限性。
Searches for anomalies are a significant motivation for the LHC and help define key analysis steps, including triggers. We discuss specific examples how LHC anomalies can be defined through probability density estimates, evaluated in a physics space or in an appropriate neural network latent space, and discuss the model-dependence in choosing an appropriate data parameterisation. We illustrate this for classical k-means clustering, a Dirichlet variational autoencoder, and invertible neural networks. For two especially challenging scenarios of jets from a dark sector we evaluate the strengths and limitations of each method.
