Utilizing machine learning to improve the precision of fluorescence imaging of cavity-generated spin squeezed states

TL;DR

This paper introduces a supervised machine learning approach to calibrate photon collection rates in fluorescence imaging of cold atoms, significantly improving measurement precision and efficiency.

Contribution

The study develops a multi-variable linear regression model that enhances calibration accuracy and robustness in fluorescence imaging, outperforming previous single-variable methods.

Findings

Measurement variance reduced by 27%

Model calibration time less than a minute

Applicable across different populations and days

Abstract

We present a supervised learning model to calibrate the photon collection rate during the fluorescence imaging of cold atoms. The linear regression model finds the collection rate at each location on the sensor such that the atomic population difference equals that of a highly precise optical cavity measurement. This 192 variable regression results in a measurement variance 27% smaller than our previous single variable regression calibration. The measurement variance is now in agreement with the theoretical limit due to other known noise sources. This model efficiently trains in less than a minute on a standard personal computer's CPU, and requires less than 10 minutes of data collection. Furthermore, the model is applicable across a large changes in population difference and across data collected on different days.