Probing ultracold gases using photoionization fine structure

TL;DR

This paper demonstrates how photoionization fine structure in ultracold gases can serve as a detailed probe of interatomic distances and scattering properties, utilizing interference effects and neural networks for analysis.

Contribution

It introduces a method to extract structural and scattering information from photoionization spectra in ultracold gases, combining interference effects with machine learning techniques.

Findings

Fine structure reveals interatomic distances and scattering details.

Neural networks can extract phase shifts from spectra.

Applicable to Rydberg molecules with relevant parameters.

Abstract

Photoionization of atoms immersed in an environment such as an ultracold gas is investigated. We show that the interference of two ionization pathways, one passing directly to the continuum and one accounting for scattering processes between the photoelectron and a neighboring atom, produces a fine structure in the photoionization cross-section over an energy range less than 1 eV above threshold. This fine structure includes all the details of the corresponding three-body system, e.g. the interatomic distance or the scattering information of the electron-atom subsystem; therefore, photoelectrons produced in a multi-particle environment can be utilized as structural probes. As an illustration, for experimentally relevant parameters, we propose a scheme based on the photoionization of a Rydberg molecule where the low-energy electron-atom phase shifts are extracted from the fine structure spectra using neural networks.