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

2025-02-25

AI Summary

Traditional non-intrusive beam loss monitors (BLMs) and beam current monitors (BCMs) yield only macroscopic, time-integrated signals, precluding direct reconstruction of the full six-dimensional (6D) phase-space structure. To address this limitation, we propose an adaptive inverse-mapping framework based on a conditional latent-variable diffusion model—enabling, for the first time, end-to-end reconstruction of two-dimensional (2D) phase-space density projections (e.g., (x ext{--}p_x), (y ext{--}p_y)) directly from multi-sensor time-series waveforms. Our method jointly models temporal dynamics across heterogeneous sensor modalities and learns phase-space density distributions, trained exclusively on high-fidelity multi-particle simulation data from the Los Alamos Neutron Science Center (LANSCE) high-current proton linac. Evaluated on kilometer-scale accelerator simulations, the approach achieves significantly enhanced phase-space resolution—surpassing conventional diagnostic accuracy—while requiring no hardware modifications. This establishes a low-cost, high-fidelity, accelerator-agnostic paradigm for phase-space diagnostics.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Mapping beam loss to phase space
• Enhancing beam diagnostics with AI
• Applying latent diffusion models in accelerators

Innovation

• Adaptive conditional latent diffusion
• Map BLM waveforms to 2D phase space
• Transform non-invasive devices into diagnostics