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

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.

References in corpus (11)

• Implementation of Cavity Squeezing of a Collective Atomic Spin
• Deterministic Squeezed States with Joint Measurements and Feedback
• Spin squeezing, entanglement and quantum metrology with Bose-Einstein condensates
• Machine learning for faster and smarter fluorescence lifetime imaging microscopy
• Probing spin correlations in a Bose-Einstein condensate near the single atom level
• Calibrating High Intensity Absorption Imaging of Ultracold Atoms
• Entangled collective-spin states of atomic ensembles under non-uniform atom-light interaction
• Fluorescence detection at the atom shot noise limit for atom interferometry
• Single-exposure absorption imaging of ultracold atoms using deep learning
• Demonstration of a free space rubidium atomic clock with noise below the quantum projection limit
• Retrieval of cavity-generated atomic spin-squeezing after free-space release