Data-Driven Gradient Optimization for Field Emission Management in a Superconducting Radio-Frequency Linac

TL;DR

This paper introduces a machine learning-based method with uncertainty quantification to predict and optimize cavity gradients in superconducting linacs, significantly reducing radiation levels caused by field emission while maintaining energy gain.

Contribution

It presents a novel data-driven approach that combines machine learning and uncertainty quantification to optimize cavity gradients and mitigate radiation in superconducting linacs.

Findings

Over 40% reduction in neutron radiation.

Over 40% reduction in gamma radiation.

Effective cavity gradient optimization maintained energy gain.

Abstract

Field emission can cause significant problems in superconducting radio-frequency linear accelerators (linacs). When cavity gradients are pushed higher, radiation levels within the linacs may rise exponentially, causing degradation of many nearby systems. This research aims to utilize machine learning with uncertainty quantification to predict radiation levels at multiple locations throughout the linacs and ultimately optimize cavity gradients to reduce field emission induced radiation while maintaining the total linac energy gain necessary for the experimental physics program. The optimized solutions show over 40% reductions for both neutron and gamma radiation from the standard operational settings.