Allan Variance Analysis as Useful Tool to Determine Noise in Various Single-Molecule Setups

TL;DR

This paper demonstrates how Allan variance analysis can effectively quantify and identify noise sources, including drift, in optical trapping setups, aiding in optimizing experimental calibration and data acquisition.

Contribution

It introduces Allan variance analysis as a practical tool to assess and minimize noise in various single-molecule optical trapping experiments.

Findings

Allan variance helps identify low-frequency drift noise.

Optimal sampling time improves calibration accuracy.

Additional data points enhance measurement precision.

Abstract

One limitation on the performance of optical traps is the noise inherently present in every setup. Therefore, it is the desire of most experimentalists to minimize and possibly eliminate noise from their optical trapping experiments. A step in this direction is to quantify the actual noise in the system and to evaluate how much each particular component contributes to the overall noise. For this purpose we present Allan variance analysis as a straightforward method. In particular, it allows for judging the impact of drift which gives rise to low-frequency noise, which is extremely difficult to pinpoint by other methods. We show how to determine the optimal sampling time for calibration, the optimal number of data points for a desired experiment, and we provide measurements of how much accuracy is gained by acquiring additional data points. Allan variances of both micrometer-sized spheres and asymmetric nanometer-sized rods are considered.