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

Understand

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.