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Deep Learning-Based Segmentation of Cryo-Electron Tomograms

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Neuro-explicit semantic segmentation of the diffusion cloud chamber.

Nicola J Müller^1,2, Daniel Porawski¹, Lukas Wilde¹

¹Bachelor's Program Data Science and Artificial Intelligence, Saarland University, Saarbrücken 66123, Germany.

The Review of Scientific Instruments

|October 20, 2023

Summary

This study introduces a novel artificial intelligence (AI) model for automatically identifying subatomic particle tracks in diffusion cloud chambers. The AI significantly improves data processing and reduces misclassification of rare particles.

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Area of Science:

Particle Physics
Artificial Intelligence
Computer Vision

Background:

Traditional diffusion cloud chambers relied on manual identification of particle tracks, limiting data throughput.
Automated analysis is crucial for advancing particle detection capabilities.

Purpose of the Study:

To develop an automated system for identifying and classifying subatomic particle tracks from diffusion cloud chamber images.
To enhance the efficiency and accuracy of particle track analysis in classical detectors.

Main Methods:

Developed a neuro-explicit artificial intelligence model.
Combined the attention U-Net neural network architecture with track shape modeling methods.
Trained and tested the model on diffusion cloud chamber images.

Main Results:

The AI model effectively detects and annotates most visible particle tracks.
The neuro-explicit approach reduced the misclassification rate of rare particles by 73% compared to using attention U-Net alone.
Demonstrated successful integration of AI into classical particle detection.

Conclusions:

The developed AI model enables digital analysis of diffusion cloud chamber data.
Neuro-explicit AI significantly improves the accuracy of rare particle identification.
This advancement allows classical particle detectors to enter the digital era with enhanced capabilities.