Variational Pseudo Marginal Methods for Jet Reconstruction in Particle Physics

• URL: http://arxiv.org/abs/2406.03242v1
• Date: Wed, 5 Jun 2024 13:18:55 GMT
• Title: Variational Pseudo Marginal Methods for Jet Reconstruction in Particle Physics
• Authors: Hanming Yang, Antonio Khalil Moretti, Sebastian Macaluso, Philippe Chlenski, Christian A. Naesseth, Itsik Pe'er,
• Abstract summary: 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. We illustrate our method's effectiveness through experiments using data generated with a collider physics generative model.
• Score: 2.223804777595989
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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