半监督置换不变粒子级异常检测
Semi-supervised permutation invariant particle-level anomaly detection
AI总结：
提出一种基于粒子流网络和自编码器的半监督异常检测方法，利用置换不变的事件级表示，在高能对撞机模拟数据上有效区分不同新物理模型。
摘要：
开发分析方法以模型无关的方式区分超越标准模型的潜在现象，可以显著提高对撞机实验中的发现能力。然而，用于此任务的典型机器学习（ML）算法需要固定长度和有序的输入，这破坏了碰撞事件中自然的置换不变性。为了解决这一问题，提出了一种半监督异常检测工具，该工具接受可变数量的粒子级输入，并利用信号模型通过粒子流网络（PFN）的监督训练将这些信息编码为置换不变的事件级表示。然后将数据事件编码为该表示，并作为输入提供给自编码器，用于粒子流潜空间上的无监督异常检测（ANTELOPE），基于低级且置换不变的输入建模对异常事件进行分类。ANTELOPE架构的性能在高能对撞机实验中强子过程的模拟样本上进行了评估，显示出区分不同新物理模型的良好能力。
英文摘要：
The development of analysis methods to distinguish potential beyond the Standard Model phenomena in a model-agnostic way can significantly enhance the discovery reach in collider experiments. However, the typical machine learning (ML) algorithms employed for this task require fixed length and ordered inputs that break the natural permutation invariance in collision events. To address this, a semi-supervised anomaly detection tool is presented that takes a variable number of particle-level inputs and leverages a signal model to encode this information into a permutation invariant, event-level representation via supervised training with a Particle Flow Network (PFN). Data events are then encoded into this representation and given as input to an autoencoder for unsupervised ANomaly deTEction on particLe flOw latent sPacE (ANTELOPE), classifying anomalous events based on a low-level and permutation invariant input modeling. Performance of the ANTELOPE architecture is evaluated on simulated samples of hadronic processes in a high energy collider experiment, showing good capability to distinguish disparate models of new physics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
