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Title: Reconstruction of ionization probabilities from spatially averaged data in N dimensions

Abstract

We present an analytical inversion technique, which can be used to recover ionization probabilities from spatially averaged data in an N-dimensional detection scheme. The solution is given as a power series in intensity. For this reason, we call this technique a multiphoton expansion (MPE). The MPE formalism was verified with an exactly solvable inversion problem in two dimensions, and probabilities in the postsaturation region, where the intensity-selective scanning approach breaks down, were recovered. In three dimensions, ionization probabilities of Xe were successfully recovered with MPE from simulated (using the Ammosov-Delone-Krainov tunneling theory) ion yields. Finally, we tested our approach with intensity-resolved benzene-ion yields, which show a resonant multiphoton ionization process. By applying MPE to this data (which were artificially averaged), the resonant structure was recovered, which suggests that the resonance in benzene may have been observed in spatially averaged data taken elsewhere.

Authors:
; ;  [1]
  1. Department of Physics, Texas A and M University, College Station, Texas 77843-4242 (United States)
Publication Date:
OSTI Identifier:
21442929
Resource Type:
Journal Article
Journal Name:
Physical Review. A
Additional Journal Information:
Journal Volume: 82; Journal Issue: 1; Other Information: DOI: 10.1103/PhysRevA.82.013403; (c) 2010 The American Physical Society; Journal ID: ISSN 1050-2947
Country of Publication:
United States
Language:
English
Subject:
74 ATOMIC AND MOLECULAR PHYSICS; BENZENE; EXACT SOLUTIONS; MOLECULAR IONS; MULTI-PHOTON PROCESSES; PHOTOIONIZATION; PHOTON-MOLECULE COLLISIONS; PROBABILITY; RESONANCE; TUNNEL EFFECT; XENON; YIELDS; AROMATICS; CHARGED PARTICLES; COLLISIONS; ELEMENTS; FLUIDS; GASES; HYDROCARBONS; IONIZATION; IONS; MATHEMATICAL SOLUTIONS; MOLECULE COLLISIONS; NONMETALS; ORGANIC COMPOUNDS; PHOTON COLLISIONS; RARE GASES

Citation Formats

Strohaber, J, Kolomenskii, A A, and Schuessler, H A. Reconstruction of ionization probabilities from spatially averaged data in N dimensions. United States: N. p., 2010. Web. doi:10.1103/PHYSREVA.82.013403.
Strohaber, J, Kolomenskii, A A, & Schuessler, H A. Reconstruction of ionization probabilities from spatially averaged data in N dimensions. United States. https://doi.org/10.1103/PHYSREVA.82.013403
Strohaber, J, Kolomenskii, A A, and Schuessler, H A. 2010. "Reconstruction of ionization probabilities from spatially averaged data in N dimensions". United States. https://doi.org/10.1103/PHYSREVA.82.013403.
@article{osti_21442929,
title = {Reconstruction of ionization probabilities from spatially averaged data in N dimensions},
author = {Strohaber, J and Kolomenskii, A A and Schuessler, H A},
abstractNote = {We present an analytical inversion technique, which can be used to recover ionization probabilities from spatially averaged data in an N-dimensional detection scheme. The solution is given as a power series in intensity. For this reason, we call this technique a multiphoton expansion (MPE). The MPE formalism was verified with an exactly solvable inversion problem in two dimensions, and probabilities in the postsaturation region, where the intensity-selective scanning approach breaks down, were recovered. In three dimensions, ionization probabilities of Xe were successfully recovered with MPE from simulated (using the Ammosov-Delone-Krainov tunneling theory) ion yields. Finally, we tested our approach with intensity-resolved benzene-ion yields, which show a resonant multiphoton ionization process. By applying MPE to this data (which were artificially averaged), the resonant structure was recovered, which suggests that the resonance in benzene may have been observed in spatially averaged data taken elsewhere.},
doi = {10.1103/PHYSREVA.82.013403},
url = {https://www.osti.gov/biblio/21442929}, journal = {Physical Review. A},
issn = {1050-2947},
number = 1,
volume = 82,
place = {United States},
year = {Thu Jul 15 00:00:00 EDT 2010},
month = {Thu Jul 15 00:00:00 EDT 2010}
}