Summary of Variational Pseudo Marginal Methods For Jet Reconstruction in Particle Physics, by Hanming Yang and Antonio Khalil Moretti and Sebastian Macaluso and Philippe Chlenski and Christian A. Naesseth and Itsik Pe’er
Variational Pseudo Marginal Methods for Jet Reconstruction in Particle Physics
by Hanming Yang, Antonio Khalil Moretti, Sebastian Macaluso, Philippe Chlenski, Christian A. Naesseth, Itsik Pe’er
First submitted to arxiv on: 5 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation (stat.CO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel approach to reconstructing jets in collider physics, which is crucial for understanding subatomic particles produced in high-energy collisions. The task involves estimating the latent structure of a jet, considering parameters such as particle energy, momentum, and types. Bayesian methods are well-suited for handling uncertainty and leveraging prior knowledge but face challenges due to the super-exponential growth of potential jet topologies. To address this, the authors introduce a Combinatorial Sequential Monte Carlo approach for inferring jet latent structures, followed by a variational inference algorithm for parameter learning. The method is unified with a pseudo-marginal framework for a fully Bayesian treatment of all variables. Experimental results demonstrate the approach’s effectiveness in generating data using a collider physics generative model, showcasing superior speed and accuracy across various tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a problem in collider physics that helps scientists understand tiny particles created in high-energy collisions. The challenge is to figure out what these particles are and how they’re connected. Bayesian methods can help with this but get overwhelmed by the many possible connections. To fix this, the authors create a new way to guess the connections using a special kind of computer simulation called Monte Carlo. This method also helps scientists learn more about the properties of the particles. The authors tested their approach and showed it works better than other methods. |
Keywords
» Artificial intelligence » Generative model » Inference
