Year: 2025
Type: preprint
Abstract: This paper reports on the use of a convolutional neural network (CNN) methodology to analyse fluorescence images of calcium-ion Coulomb crystals in the gas phase. A transfer-learning approach is adopted using the publicly available RESNET50 model. It is demonstrated that by training the neural network on around 500,000 simulated images, we are able to determine ion-numbers not only for a verification set of 100,000 simulated images, but also for experimental calcium-ion images from two different laboratories using a wide range of ion-trap parameters. Absolute ion numbers in the crystal were determined for the experimental data with a percentage error of approximately 10%. This analysis can be performed in a few seconds for an individual crystal image, and therefore the method enables the objective, and efficient, analysis of such images in real time, thereby facilitating time-dependent kinetic measurements on ion-molecule chemistry. The approach adopted also shows promising performance for identifying Ca+ ion numbers in images of mixed-species crystals.
Source: arXiv (Cornell University)
Authors: Allsopp, James, Diprose, Jake, Heazlewood, Brianna R., Zagorec-Marks, Chase, Lewandowski, H. J., Petralia, Lorenzo S., Softley, Timothy P.
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6979357452
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