Signal estimation and uncertainties extraction in TeraHertz Time Domain Spectroscopy

TL;DR

This paper presents a new methodology for noise reduction and uncertainty estimation in Terahertz Time Domain Spectroscopy, aiming to standardize data processing and improve material parameter extraction.

Contribution

It introduces a noise reduction technique and covariance matrix estimation method tailored for THz-TDS, addressing limited measurement scenarios and promoting standardization.

Findings

Enhanced signal-to-noise ratio in THz-TDS data

Reliable uncertainty quantification through covariance matrix estimation

Facilitates accurate material parameter extraction

Abstract

Terahertz Time Domain Spectroscopy (THz-TDS) systems have emerged as mature technologies with significant potential across various research fields and industries. However, the lack of standardized methods for signal and noise estimation and reduction hinders its full potential. This paper introduces a methodology to significantly reduce noise in THz-TDS time traces, providing a reliable and less biased estimation of the signal. The method results in an improved signal-to-noise ratio, enabling the utilization of the full dynamic range of such setups. Additionally, we investigate the estimation of the covariance matrix to quantify the uncertainties associated with the signal estimator. This matrix is essential for extracting accurate material parameters by normalizing the error function in the fitting process. Our approach addresses practical scenarios where the number of repeated measurements is limited compared to the sampling time axis length. We envision this work as the initial step toward standardizing THz-TDS data processing. We believe it will foster collaboration between the THz and signal processing communities, leading to the development of more sophisticated methods to tackle new challenges introduced by novel setups based on optoelectronic devices and dual-comb spectroscopy.