Reconstructing jets, which provide vital insights into the properties and
histories of subatomic particles produced in high-energy collisions, is a main
problem in data analyses in collider physics. This intricate task deals with
estimating the latent structure of a jet (binary tree) and involves parameters
such as particle energy, momentum, and types. While Bayesian methods offer a
natural approach for handling uncertainty and leveraging prior knowledge, they
face significant challenges due to the super-exponential growth of potential
jet topologies as the number of observed particles increases. To address this,
we introduce a Combinatorial Sequential Monte Carlo approach for inferring jet
latent structures. As a second contribution, we leverage the resulting
estimator to develop a variational inference algorithm for parameter learning.
Building on this, we introduce a variational family using a pseudo-marginal
framework for a fully Bayesian treatment of all variables, unifying the
generative model with the inference process. We illustrate our method's
effectiveness through experiments using data generated with a collider physics
generative model, highlighting superior speed and accuracy across a range of
tasks.

使用重构喷注、贝叶斯方法、序贯蒙特卡罗、变分推断和对撞机物理等关键词，介绍了一种用于推断喷注潜在结构的组合序贯蒙特卡罗方法，以及该方法使用估计器进行参数学习和提供全面贝叶斯处理的可变推理算法。通过使用按照碰撞机物理生成模型生成的数据进行实验证明了该方法在速度和准确性上的优越性。