Bayesian estimation for selective trace gas detection

TL;DR

This paper introduces a Bayesian estimation method for selective trace gas detection using differential diffusion and optical absorption detectors, enabling accurate determination of gas species populations from diffusion profiles.

Contribution

It presents a novel Bayesian analysis approach for trace gas detection that leverages differential diffusion and optical absorption data to estimate species populations.

Findings

Effective estimation of gas species populations from diffusion profiles

Improved accuracy in trace gas detection using Bayesian methods

Applicable to high-finesse Fabry-Perot cavity setups

Abstract

We present a Bayesian estimation analysis for a particular trace gas detection technique with species separation provided by differential diffusion. The proposed method collects a sample containing multiple gas species into a common volume, and then allows it to diffuse across a linear array of optical absorption detectors, using, for example, high-finesse Fabry-Perot cavities. The estimation procedure assumes that all gas parameters (e.g. diffusion constants, optical cross sections) are known except for the number population of each species, which are determined from the time-of-flight absorption profiles in each detector.