Related papers: Adaptive conditional latent diffusion maps beam loss to 2D phase space projections

Adaptive conditional latent diffusion maps beam loss to 2D phase space projections

URL: http://arxiv.org/abs/2502.18684v1
Date: Tue, 25 Feb 2025 22:46:27 GMT
Title: Adaptive conditional latent diffusion maps beam loss to 2D phase space projections
Authors: Alexander Scheinker, Alan Williams,
Abstract summary: Beam loss (BLM) and beam current monitors (BCM) are ubiquitous at particle accelerator around the world.<n>We show that generative conditional latent diffusion models can learn intricate patterns to map waveforms of tens of BLMs or BCMs along an accelerator to detailed 2D projections of a charged particle beam's 6D phase space density.
Score: 49.1574468325115
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Beam loss (BLM) and beam current monitors (BCM) are ubiquitous at particle accelerator around the world. These simple devices provide non-invasive high level beam measurements, but give no insight into the detailed 6D (x,y,z,px,py,pz) beam phase space distributions or dynamics. We show that generative conditional latent diffusion models can learn intricate patterns to map waveforms of tens of BLMs or BCMs along an accelerator to detailed 2D projections of a charged particle beam's 6D phase space density. This transformational method can be used at any particle accelerator to transform simple non-invasive devices into detailed beam phase space diagnostics. We demonstrate this concept via multi-particle simulations of the high intensity beam in the kilometer-long LANSCE linear proton accelerator.

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