Beam loss modeling and mitigation due to intra-beam stripping

Intra-Beam Stripping (IBS) is a critical beam loss mechanism in high-intensity H- linacs and presents a significant limitation to increasing beam power. This work presents a computational framework to evaluate and mitigate IBS-induced beam loss along the Spallation Neutron Source (SNS) LINAC. Our calculation is based on an analytic theory and involves evaluation of a 9D integral using the Monte-Carlo technique. We first benchmarked our calculations against simplified, analytically solvable cases. We then applied our algorithm to Gaussian bunches with a known probability density function (PDF). We next expanded our algorithm to arbitrary bunch distributions using the Neural Spline Flow (NSF) models trained on PyORBIT tracking data. In the future, we plan to validate our algorithm experimentally and apply it to design IBS mitigation strategies.

Authors

Publication Details

Journal
JACOW
Published
2026-01-29
DOI
https://doi.org/10.18429/jacow-napac2025-thp014
Primary Topic
Particle accelerators and beam dynamics
Type
article
Field-Weighted Citation Impact
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article

Beam loss modeling and mitigation due to intra-beam stripping

Austin Hoover, Vasiliy Morozov, Lawrence Lee, Shivam Kakkar et al.
JACOW
Particle accelerators and beam dynamics
article

Beam loss modeling and mitigation due to intra-beam stripping

Austin Hoover, Vasiliy Morozov, Lawrence Lee, Shivam Kakkar, Alexander Zhukov, Nicholas Evans
article en

Abstract

Intra-Beam Stripping (IBS) is a critical beam loss mechanism in high-intensity H- linacs and presents a significant limitation to increasing beam power. This work presents a computational framework to evaluate and mitigate IBS-induced beam loss along the Spallation Neutron Source (SNS) LINAC. Our calculation is based on an analytic theory and involves evaluation of a 9D integral using the Monte-Carlo technique. We first benchmarked our calculations against simplified, analytically solvable cases. We then applied our algorithm to Gaussian bunches with a known probability density function (PDF). We next expanded our algorithm to arbitrary bunch distributions using the Neural Spline Flow (NSF) models trained on PyORBIT tracking data. In the future, we plan to validate our algorithm experimentally and apply it to design IBS mitigation strategies.

JACOW
Openalex Percentile: Top 75%
Particle accelerators and beam dynamics
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