Attosecond Pulse Retrieval From Noisy Streaking Traces With Conditional Variational Generative Network

Abstract

Accurate characterization of an attosecond pulse from streaking trace is an indispensable step in studying the ultrafast electron dynamics on the attosecond scale. Conventional attosecond pulse retrieval methods face two major challenges: the ability to incorporate a complete physics model of the streaking process, and the ability to model the uncertainty of pulse reconstruction in the presence of noise. Here we propose a pulse retrieval method based on conditional variational generative network (CVGN) that can address both demands.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 2020
Accession Number
AD1100969

Entities

People

  • Jonathon White
  • Shuo Pang
  • Zenghu Chang
  • Zheyuan Zhu

Organizations

  • University of Central Florida

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Broadband
  • Data Acquisition
  • Dipole Moments
  • Electrons
  • Errors
  • Frequency
  • Frequency Domain
  • Measurement
  • Neural Networks
  • Photoelectrons
  • Physics
  • Soft X Rays
  • Spectra
  • Time Domain
  • Wave Packets
  • X Rays

Readers

  • Materials Science (Mechanical Engineering).
  • Neural Network Machine Learning.
  • Quantum Dot Semiconductor Device Photonics and Graphene Optoelectronic Materials and THz Physics.

Technology Areas

  • Microelectronics