Analysis of femtosecond pump-probe photoelectron-photoion coincidence measurements applying Bayesian probability theory

TL;DR

This paper introduces a Bayesian probabilistic method for analyzing femtosecond pump-probe photoelectron-photoion coincidence data, improving signal extraction, noise handling, and false coincidence rejection, thereby enabling faster and more accurate molecular dynamics studies.

Contribution

The authors develop a Bayesian framework that enhances data analysis in pump-probe coincidence experiments, addressing background signals and false coincidences more effectively than traditional subtraction methods.

Findings

Significantly increased signal-to-noise ratio.

Accurate reconstruction of excited-state spectra.

Reduction in data acquisition times.

Abstract

Ultrafast dynamical processes in photoexcited molecules can be observed with pump-probe measurements, in which information about the dynamics is obtained from the transient signal associated with the excited state. Background signals provoked by pump and/or probe pulses alone often obscure these excited state signals. Simple subtraction of pump-only and/or probe-only measurements from the pump-probe measurement, as commonly applied, results in a degradation of the signal-to-noise ratio and, in the case of coincidence detection, the danger of overrated background subtraction. Coincidence measurements additionally suffer from false coincidences. Here we present a probabilistic approach based on Bayesian probability theory that overcomes these problems. For a pump-probe experiment with photoelectron-photoion coincidence detection we reconstruct the interesting excited-state spectrum from pump-probe and pump-only measurements. This approach allows to treat background and false coincidences consistently and on the same footing. We demonstrate that the Bayesian formalism has the following advantages over simple signal subtraction: (i) the signal-to-noise ratio is significantly increased, (ii) the pump-only contribution is not overestimated, (iii) false coincidences are excluded, (iv) prior knowledge, such as positivity, is consistently incorporated, (v) confidence intervals are provided for the reconstructed spectrum, and (vi) it is applicable to any experimental situation and noise statistics. Most importantly, by accounting for false coincidences, the Bayesian approach allows to run experiments at higher ionization rates, resulting in a significant reduction of data acquisition times. The application to pump-probe coincidence measurements on acetone molecules enables novel quantitative interpretations about the molecular decay dynamics and fragmentation behavior.