2018•JACOWOpen access

Recent On-Line Taper Optimization on LCLS

Juhao Wu, Xiaobiao Huang, Tor O. Raubenheimer, Alexander Scheinker

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Abstract

High-brightness XFELs are demanding for many users, in particular for certain types of imaging applications. Self-seeding XFELs can respond to a heavily tapered undulator more effectively, therefore seeded tapered FELs are considered as a path to high-power FELs in the terawatts level. Due to many effects, including the synchrotron motion, the optimization of the taper profile is intrinsically multi-dimensional and computationally expensive. With an operating XFEL, such as LCLS, the on-line optimization becomes more economical than numerical simulation. Here we report recent on-line taper optimization on LCLS taking full advantages of nonlinear optimizers as well as up-to-date development of artificial intelligence: deep machine learning and neural networks.

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What this paper is about

High-brightness XFELs are demanding for many users, in particular for certain types of imaging applications. Self-seeding XFELs can respond to a heavily tapered undulator more effectively, therefore seeded tapered FELs are considered as a path to high-power FELs in the terawatts level. Due to many effects, including the synchrotron motion, the optimization of the taper profile is intrinsically multi-dimensional and computationally expensive. With an operating XFEL, such as LCLS, the on-line optimization becomes more economical than numerical simulation. Here we report recent on-line taper optimization on LCLS taking full advantages of nonlinear optimizers as well as up-to-date development of artificial intelligence: deep machine learning and neural networks.

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Available abstract

High-brightness XFELs are demanding for many users, in particular for certain types of imaging applications. Self-seeding XFELs can respond to a heavily tapered undulator more effectively, therefore seeded tapered FELs are considered as a path to high-power FELs in the terawatts level. Due to many effects, including the synchrotron motion, the optimization of the taper profile is intrinsically multi-dimensional and computationally expensive. With an operating XFEL, such as LCLS, the on-line optimization becomes more economical than numerical simulation. Here we report recent on-line taper optimization on LCLS taking full advantages of nonlinear optimizers as well as up-to-date development of artificial intelligence: deep machine learning and neural networks.

Key concepts: Line (geometry), Computer science, Mathematics, Geometry

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