High-energy collisions at the Large Hadron Collider (LHC) provide valuable insights into open questions in particle physics. However, detector effects must be corrected before measurements can be compared to certain theoretical predictions or measurements from other detectors. Methods to solve this \textit{inverse problem} of mapping detector observations to theoretical quantities of the underlying collision are essential parts of many physics analyses at the LHC. We investigate and compare various generative deep learning methods to approximate this inverse mapping. We introduce a novel unified architecture, termed latent variation diffusion models, which combines the latent learning of cutting-edge generative art approaches with an end-to-end variational framework. We demonstrate the effectiveness of this approach for reconstructing global distributions of theoretical kinematic quantities, as well as for ensuring the adherence of the learned posterior distributions to known physics constraints. Our unified approach achieves a distribution-free distance to the truth of over 20 times less than non-latent state-of-the-art baseline and 3 times less than traditional latent diffusion models.

翻译：大型强子对撞机（LHC）的高能碰撞为理解粒子物理中的未解问题提供了宝贵见解。然而，在将测量结果与特定理论预测或其他探测器的测量数据进行比较之前，必须校正探测器效应。解决这一将探测器观测映射为底层碰撞理论量的反问题的方法，是LHC许多物理分析中的关键组成部分。我们研究并比较了多种用于近似该逆映射的生成式深度学习方法。我们提出了一种新颖的统一架构，称为潜变量变分扩散模型，该模型将前沿生成式艺术方法中的潜空间学习与端到端变分框架相结合。我们证明了该方法在重建理论运动学量的全局分布，以及确保学习到的后验分布符合已知物理约束方面的有效性。我们的统一方法实现了比非潜空间最先进基线模型低20倍以上、比传统潜扩散模型低3倍的无分布距离到真实值的误差。