High-dimensional maximum-entropy phase space tomography using normalizing flows

Austin Hoover, Jonathan C. Wong
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引用次数: 0

Abstract

Particle accelerators generate charged-particle beams with tailored distributions in six-dimensional position-momentum space (phase space). Knowledge of the phase space distribution enables model-based beam optimization and control. In the absence of direct measurements, the distribution must be tomographically reconstructed from its projections. In this paper, we highlight that such problems can be severely underdetermined and that entropy maximization is the most conservative solution strategy. We leverage normalizing flows—invertible generative models—to extend maximum-entropy tomography to six-dimensional phase space and perform numerical experiments to validate the model's performance. Our numerical experiments demonstrate consistency with exact two-dimensional maximum-entropy solutions and the ability to fit complicated six-dimensional distributions to large measurement sets in reasonable time.

Abstract Image

利用归一化流动的高维最大熵相空间断层成像技术
粒子加速器产生的带电粒子束在六维位置-动量空间(相空间)中具有量身定制的分布。了解了相空间的分布情况,就能对粒子束进行基于模型的优化和控制。在缺乏直接测量的情况下,必须根据其投影对分布进行层析重建。在本文中,我们强调此类问题可能会严重欠定,而熵最大化是最保守的求解策略。我们利用归一化流-可逆生成模型,将最大熵层析扩展到六维相空间,并通过数值实验验证了该模型的性能。我们的数值实验证明了与精确的二维最大熵解的一致性,以及在合理时间内将复杂的六维分布拟合到大型测量集的能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
8.60
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