Adaptive Machine Learning for Time-Varying Systems: Towards 6D Phase Space Diagnostics of Short Intense Charged Particle Beams

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.