Variational Pseudo Marginal Methods for Jet Reconstruction in Particle Physics
This work addresses a domain-specific challenge in collider physics by improving jet reconstruction, though it is incremental as it builds on existing Bayesian and variational methods.
The paper tackled the problem of reconstructing jets in particle physics, which involves estimating latent structures and parameters from high-energy collision data, by introducing a Combinatorial Sequential Monte Carlo approach and a variational inference algorithm, achieving superior speed and accuracy in experiments.
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.