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

TL;DR

This paper introduces a novel generative model that transforms simple beam loss and current measurements into detailed 2D phase space projections, enhancing diagnostics at particle accelerators.

Contribution

The paper presents a new application of conditional latent diffusion models to map non-invasive measurements to detailed 6D beam phase space projections.

Findings

Successfully applied to multi-particle simulations of LANSCE accelerator

Demonstrates potential for real-time beam diagnostics

Enables detailed phase space analysis from simple measurements

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.