Extraction and identification of noise patterns for ultracold atoms in an optical lattice

TL;DR

This paper demonstrates how principal component analysis can be used to identify and separate different noise sources in ultracold atom experiments, specifically in Bose-Einstein condensates in optical lattices, aiding in noise reduction.

Contribution

The study applies PCA to experimental data of ultracold atoms to successfully distinguish and attribute noise modes to their physical origins, enhancing data analysis methods.

Findings

PCA can decompose fluctuations into eigenmodes in cold atom experiments.

Noise sources from different physical origins can be separated using PCA.

Numerical analysis confirms the physical attribution of identified noise modes.

Abstract

To extract useful information about quantum effects in cold atom experiments, one central task is to identify the intrinsic quantum fluctuation from extrinsic system noises of various kinds. As a data processing method, principal component analysis can decompose fluctuations in experimental data into eigen modes, and give a chance to separate noises originated from different physical sources. In this paper, we demonstrate for Bose-Einstein condensates in one-dimensional optical lattices that the principal component analysis can be applied to time-of-flight images to successfully separate and identify noises from different origins of leading contribution, and can help to reduce or even eliminate noises via corresponding data processing procedures. The attribution of noise modes to their physical origins is also confirmed by numerical analysis within a mean-field theory.