Variational Pseudo Marginal Methods for Jet Reconstruction in Particle Physics
<|reference_start|>Variational Pseudo Marginal Methods for Jet Reconstruction in Particle Physics: 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.<|reference_end|>
@article{yang2024variational,
    title={Variational Pseudo Marginal Methods for Jet Reconstruction in Particle
  Physics},
    author={Hanming Yang and Antonio Khalil Moretti and Sebastian Macaluso and
  Philippe Chlenski and Christian A. Naesseth and Itsik Pe'er},
    journal={arXiv preprint arXiv:2406.03242},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.03242},
    primaryClass={cs.LG stat.CO}
}