Convolutional neural network approach to ion Coulomb crystal image analysis

Area of Science:

• Atomic, Molecular, and Optical Physics
• Computational Physics
• Chemical Physics

Background:

• Calcium-ion Coulomb crystals are crucial for studying cold ion-molecule reactions.
• Accurate determination of ion numbers in these crystals is experimentally challenging.
• Existing methods for image analysis can be time-consuming and subjective.

Purpose of the Study:

• To develop an efficient and objective method for analyzing fluorescence images of calcium-ion Coulomb crystals.
• To accurately determine the number of ions in both simulated and experimental crystal images.
• To enhance the study of cold ion-molecule reaction kinetics and dynamics.

Main Methods:

• Utilized a convolutional neural network (CNN) methodology for image analysis.
• Employed a transfer-learning approach with the pre-trained RESNET50 model.
• Retrained the CNN on approximately 500,000 simulated images of calcium-ion crystals.

Main Results:

• Achieved accurate ion number determination for a validation set of 100,000 simulated images.
• Successfully analyzed experimental calcium-ion images from two laboratories with ~10% error.
• Demonstrated real-time analysis capability, with individual image processing in seconds.
• Showed promising results for identifying ion numbers in mixed-species crystals.

Conclusions:

• The developed CNN methodology offers an objective, efficient, and accurate approach for analyzing calcium-ion Coulomb crystal images.
• This method significantly enhances experimental capabilities for studying cold ion-molecule reactions.
• The real-time analysis enables faster data processing and potentially new experimental insights.