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

arXiv: https://arxiv.org/abs/2502.18684 · HTML: https://mlsanity.com/paper/2502.18684/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Flow matching, time series and science, one of 14 directions in Diffusion models.
- Builds on 3 of its 13 references that are in the map.
- First posted on arXiv 25 Feb 2025.

## Placed in

- Diffusion models / [Flow matching, time series and science](https://mlsanity.com/diffusion/flow-matching-time-series-and-science/)

## Builds on (most cited first, 3 of 3)

- [Online Multi-Objective Particle Accelerator Optimization of the AWAKE Electron Beam Line for Simultaneous Emittance and Orbit Control](https://mlsanity.com/paper/2003.11155/) (2020)
- [Extraction of instantaneous frequencies and amplitudes in nonstationary time-series data](https://mlsanity.com/paper/2104.01293/) (2021)
- [Physics-Informed Super-Resolution Diffusion for 6D Phase Space Diagnostics](https://mlsanity.com/paper/2501.04305/) (2025)

## Abstract (arXiv)

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.