论文标题
时间变化系统的自适应机业学习：朝着6D阶段空间诊断诊断，短期电荷颗粒梁的诊断
Adaptive Machine Learning for Time-Varying Systems: Towards 6D Phase Space Diagnostics of Short Intense Charged Particle Beams
论文作者
论文摘要
随着带电的粒子束变得更短，更激烈，非线性束内集体相互作用（例如太空电荷力和束对束影响）的影响，例如韦克菲尔德和连贯的同步辐射。较短的束束也更难准确地映像，因为它们的尺寸超出了现有诊断的解决方案，并且可能会破坏截止诊断。对于强烈的高能梁而言，详细诊断的可用性有限，这对加速器社区来说是一个基本挑战，因为光束和加速器都是时变的系统，它们以无法预测的方式改变。详细的6D（X，Y，Z，PX，PY，PY，PZ）分布从来源出现的光束随时间而变化，这是由于不断发展的光电阴极激光强度曲线和光电阴极的量子效率等因素而变化。加速器磁铁，RF放大器和控制系统受到外部干扰，梁加载效果，温度变化和未对准的扰动。尽管最近几年的加速器社区中的机器学习（ML）方法在越来越多，但在涉及时变的系统中，它们在根本上受到了限制，而这些系统大多数当前的方法都只是简单地收集大型新数据集并进行重新训练，这对于忙碌的加速器用户设施而言并不容易，因为详细的横梁测量通常中断了。 New adaptive machine learning (AML) methods designed for time-varying systems are needed to aid in the diagnostics and control of high-intensity, ultrashort beams by combining deep learning tools such as convolutional neural network-based encoder-decoder architectures, model-independent feedback, physics constraints, and online models with real time non-invasive beam data, to provide a detailed virtual view of intense bunch dynamics.
As charged particle bunches become shorter and more intense, the effects of nonlinear intra-bunch collective interactions such as space charge forces and bunch-to-bunch influences such as wakefields and coherent synchrotron radiation also increase. Shorter more intense bunches are also more difficult to accurately image because their dimensions are beyond the resolution of existing diagnostics and they may be destructive to intercepting diagnostics. The limited availability of detailed diagnostics for intense high energy beams is a fundamental challenge for the accelerator community because both beams and accelerators are time-varying systems that change in unpredictable ways. The detailed 6D (x,y,z,px,py,pz) distributions of beams emerging from sources vary with time due to factors such as evolving photocathode laser intensity profiles and the quantum efficiency of photocathodes. Accelerator magnets, RF amplifiers, and control systems are perturbed by external disturbances, beam-loading effects, temperature variations, and misalignments. Although machine learning (ML) methods have grown in popularity in the accelerator community in recently years, they are fundamentally limited when it comes to time-varying systems for which most current approaches are to simply collect large new data sets and perform re-training, something which is not feasible for busy accelerator user facilities because detailed beam measurements usually interrupt operations. New adaptive machine learning (AML) methods designed for time-varying systems are needed to aid in the diagnostics and control of high-intensity, ultrashort beams by combining deep learning tools such as convolutional neural network-based encoder-decoder architectures, model-independent feedback, physics constraints, and online models with real time non-invasive beam data, to provide a detailed virtual view of intense bunch dynamics.
