Variational Pseudo Marginal Methods for Jet Reconstruction in Particle Physics

TL;DR

This paper introduces a novel Bayesian inference framework combining combinatorial sequential Monte Carlo and variational methods to efficiently reconstruct jet structures in particle physics, improving speed and accuracy.

Contribution

It presents a new combinatorial sequential Monte Carlo approach and a variational inference algorithm for fully Bayesian jet reconstruction, addressing prior computational challenges.

Findings

Demonstrates superior speed over existing methods

Achieves higher accuracy in jet structure inference

Validates approach on simulated collider data

Abstract

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.