A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry

2025-07-23

AI Summary

Predicting multipactor breakdown susceptibility in high-power RF devices and accelerator components is computationally expensive and suffers from poor generalization across material domains. Method: This work introduces the first supervised machine learning framework tailored for planar geometries, trained on fully parametrized electromagnetic–particle-in-cell simulation data. It integrates principal component analysis and cross-validation to systematically evaluate Random Forest, Extra Trees, XGBoost, and funnel-structured MLP models. A novel multi-objective Bayesian optimization strategy is proposed, coupled with a joint IoU–SSIM loss function to enhance cross-material generalization. Contribution/Results: Tree-based models achieve optimal cross-material prediction performance; the multi-objectively optimized MLP significantly outperforms single-objective baselines, attaining SSIM and Pearson correlation coefficients exceeding 0.95. These results validate the efficacy of data-driven approaches for multipactor modeling and establish a new paradigm for intelligent RF system design in accelerators.

Technology Category

• Machine Learning: Multimodal Learning
• Search and Optimization: Sampling/Simulation-based Search
• Computer Vision: Multi-modal Vision

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: ML for personalized search and recommendations
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Multipactor is a nonlinear electron avalanche phenomenon that can severely impair the performance of high-power radio frequency (RF) devices and accelerator systems. Accurate prediction of multipactor susceptibility across different materials and operational regimes remains a critical yet computationally intensive challenge in accelerator component design and RF engineering. This study presents the first application of supervised machine learning (ML) for predicting multipactor susceptibility in two-surface planar geometries. A simulation-derived dataset spanning six distinct secondary electron yield (SEY) material profiles is used to train regression models - including Random Forest (RF), Extra Trees (ET), Extreme Gradient Boosting (XGBoost), and funnel-structured Multilayer Perceptrons (MLPs) - to predict the time-averaged electron growth rate, $δ_{avg}$. Performance is evaluated using Intersection over Union (IoU), Structural Similarity Index (SSIM), and Pearson correlation coefficient. Tree-based models consistently outperform MLPs in generalizing across disjoint material domains. MLPs trained using a scalarized objective function that combines IoU and SSIM during Bayesian hyperparameter optimization with 5-fold cross-validation outperform those trained with single-objective loss functions. Principal Component Analysis reveals that performance degradation for certain materials stems from disjoint feature-space distributions, underscoring the need for broader dataset coverage. This study demonstrates both the promise and limitations of ML-based multipactor prediction and lays the groundwork for accelerated, data-driven modeling in advanced RF and accelerator system design.

Problem

• Predict multipactor susceptibility in RF devices
• Reduce computational cost in accelerator design
• Improve accuracy with machine learning models

Innovation

• Supervised machine learning predicts multipactor susceptibility
• Tree-based models outperform MLPs in material generalization
• Bayesian hyperparameter optimization enhances MLP performance