学习标准模型流形：基于贝叶斯潜在扩散的碰撞异常检测
Learning the Standard Model Manifold: Bayesian Latent Diffusion for Collider Anomaly Detection
AI总结：
本文提出基于贝叶斯潜在扩散模型的碰撞数据异常检测框架，结合概率编码与潜在空间扩散动力学，通过物理约束提升密度估计稳定性，通过实验验证其在LHC喷注数据中的有效性。
摘要：
我们提出了一种基于贝叶斯潜在扩散模型的碰撞数据异常检测框架。我们的方法结合了概率编码与潜在空间中的扩散动力学，允许在保持物理约束（如质量去相关和潜在相关性正则化）的同时实现稳定的密度估计。我们训练和测试模型于模拟的LHC喷注数据，并使用种子平均ROC曲线和发现导向的度量评估其性能。通过一系列消融研究，我们表明扩散过程、贝叶斯正则化和物理驱动的损失项以互补的方式贡献：它们有助于稳定训练并提高泛化能力，即使在峰值性能提升较小时也是如此。总体而言，我们的结果强调了在构建可靠的异常检测方法时，结合不确定性估计和物理一致性的重要性，尤其是在高能物理新物理搜索中。
英文摘要：
We propose a physics-informed anomaly detection framework for collider data based on a Bayesian latent diffusion model. Our method combines a probabilistic encoder with diffusion dynamics in the latent space, allowing for stable and flexible density estimation while explicitly enforcing physics constraints, such as mass decorrelation and regularization of latent correlations. We train and test the model on simulated LHC jet data and evaluate its performance using seed-averaged ROC curves together with discovery-oriented metrics. Through a series of ablation studies, we show that the diffusion process, Bayesian regularization, and physics-motivated loss terms each contribute in a complementary way: they help stabilize training and improve generalization, even when the gains in peak performance are moderate. Overall, our results emphasize the importance of incorporating both uncertainty estimates and physics consistency when building reliable anomaly detection methods for new Physics searches in high-energy physics.
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