Login / Signup

Influence of long-range Coulomb interaction in velocity map imaging.

T BarillotR BrédyG CelepS CohenI CompagnonB ConcinaE ConstantS DanakasP KalaitzisG KarrasF LépineVincent LoriotA MarciniakG Predelus-RenoisBaptiste SchindlerC Bordas
Published in: The Journal of chemical physics (2018)
The standard velocity-map imaging (VMI) analysis relies on the simple approximation that the residual Coulomb field experienced by the photoelectron ejected from a neutral or ion system may be neglected. Under this almost universal approximation, the photoelectrons follow ballistic (parabolic) trajectories in the externally applied electric field, and the recorded image may be considered as a 2D projection of the initial photoelectron velocity distribution. There are, however, several circumstances where this approximation is not justified and the influence of long-range forces must absolutely be taken into account for the interpretation and analysis of the recorded images. The aim of this paper is to illustrate this influence by discussing two different situations involving isolated atoms or molecules where the analysis of experimental images cannot be performed without considering long-range Coulomb interactions. The first situation occurs when slow (meV) photoelectrons are photoionized from a neutral system and strongly interact with the attractive Coulomb potential of the residual ion. The result of this interaction is the formation of a more complex structure in the image, as well as the appearance of an intense glory at the center of the image. The second situation, observed also at low energy, occurs in the photodetachment from a multiply charged anion and it is characterized by the presence of a long-range repulsive potential. Then, while the standard VMI approximation is still valid, the very specific features exhibited by the recorded images can be explained only by taking into consideration tunnel detachment through the repulsive Coulomb barrier.
Keyphrases
  • deep learning
  • convolutional neural network
  • high resolution
  • blood flow
  • optical coherence tomography
  • machine learning
  • ionic liquid
  • computed tomography
  • human health
  • high density
  • climate change
  • data analysis