Model-Independent Machine Learning Approach for Nanometric Axial Localization and Tracking
Journal:
arXiv
Published Date:
May 20, 2025
Abstract
Accurately tracking particles and determining their position along the
optical axis is a major challenge in optical microscopy, especially when
extremely high precision is needed. In this study, we introduce a deep learning
approach using convolutional neural networks (CNNs) that can determine axial
positions from dual-focal plane images without relying on predefined models.
Our method achieves an axial localization accuracy of 40 nanometers - six times
better than traditional single-focal plane techniques. The model's simple
design and strong performance make it suitable for a wide range of uses,
including dark matter detection, proton therapy for cancer, and radiation
protection in space. It also shows promise in fields like biological imaging,
materials science, and environmental monitoring. This work highlights how
machine learning can turn complex image data into reliable, precise
information, offering a flexible and powerful tool for many scientific
applications.
