Maximizing the information gain of a single ion microscope using bayes experimental design

TL;DR

This paper demonstrates nanoscopic transmission microscopy using a deterministic calcium ion source and Bayes experimental design to optimize information gain, significantly improving image quality and parameter estimation over conventional methods.

Contribution

It introduces a deterministic ion source combined with Bayesian experimental design for enhanced imaging and parameter determination in nanoscopic transmission microscopy.

Findings

Suppressed detector dark counts by six orders of magnitude.

Achieved optimized imaging of transmissive structures.

Demonstrated precise parameter estimation for 1D and 2D structures.

Abstract

We show nanoscopic transmission microscopy, using a deterministic single particle source and compare the resulting images in terms of signal-to-noise ratio, with those of conventional Poissonian sources. Our source is realized by deterministic extraction of laser-cooled calcium ions from a Paul trap. Gating by the extraction event allows for the suppression of detector dark counts by six orders of magnitude. Using the Bayes experimental design method, the deterministic characteristics of this source are harnessed to maximize information gain, when imaging structures with a parametrizable transmission function. We demonstrate such optimized imaging by determining parameter values of one and two dimensional transmissive structures.