Deep learning reconstruction of attosecond X-ray pulses from an angularly streaked 2D photoelectron momentum distribution
POSTER
Abstract
We present a deep neural network (NN) to reconstruct attosecond X-ray pulses using the photoelectron momentum spectra (PEMS) from a two-color (X-ray/IR) field. A circularly polarized IR field maps the temporal profile of the X-ray pulse onto the PEMS, which is projected onto a 2D detector using a coaxial velocity map imaging spectrometer (cVMI). Our NN uses the 2D PEMS to predict the electric field of the X-ray pulse. We trained the 5-layer, fully connected network on simulated cVMI data, and tested the NN on experimental cVMI data taken at the Linac Coherent Light Source to benchmark against existing techniques. NN reconstruction of attosecond pulses from such cVMI projections allows for fast characterization of pulses, with possible application to real-time pulse diagnosis at XFELs.
*This work was primarily supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Accelerator and Detector research program as well as the Chemical Sciences, Geosciences, and Biosciences Division. Use of the Linac Coherent Light Source (LCLS), SLAC National Accelerator Laboratory, is supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences under Contract No. DE-AC02-76SF00515.
Presenters
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Paris L Franz
- Department of Applied Physics, Stanford University