Optimal Estimation of Several Linear Parameters in the Presence of Lorentzian Thermal Noise

TL;DR

This paper extends optimal estimation techniques for harmonic oscillators under thermal noise to multiple parameters, comparing with approximate methods and analyzing performance under different noise conditions.

Contribution

It introduces a generalized matrix-based approach for optimal multi-parameter estimation from continuous data in thermal noise environments.

Findings

Optimal estimators outperform approximate methods in variance suppression.

The approach effectively handles white and 1/f noise.

Method aids in experimental design and parameter precision assessment.

Abstract

In a previous article we developed an approach to the optimal (minimum variance, unbiased) statistical estimation technique for the equilibrium displacement of a damped, harmonic oscillator in the presence of thermal noise. Here, we expand that work to include the optimal estimation of several linear parameters from a continuous time series. We show that working in the basis of the thermal driving force both simplifies the calculations and provides additional insight to why various approximate (not optimal) estimation techniques perform as they do. To illustrate this point, we compare the variance in the optimal estimator that we derive for thermal noise with those of two approximate methods which, like the optimal estimator, suppress the contribution to the variance that would come from the irrelevant, resonant motion of the oscillator. We discuss how these methods fare when the dominant noise process is either white displacement noise or noise with power spectral density that is inversely proportional to the frequency ($1/f$ noise). We also construct, in the basis of the driving force, an estimator that performs well for a mixture of white noise and thermal noise. To find the optimal multi-parameter estimators for thermal noise, we derive and illustrate a generalization of traditional matrix methods for parameter estimation that can accommodate continuous data. We discuss how this approach may help refine the design of experiments as they allow an exact, quantitative comparison of the precision of estimated parameters under various data acquisition and data analysis strategies.