# Bayesian Analysis of Femtosecond Pump-Probe Photoelectron-Photoion Coincidence Spectra with Fluctuating Laser Intensities

## TL;DR

This paper advances Bayesian data analysis methods for femtosecond pump-probe photoelectron-photoion coincidence experiments, improving background subtraction, false coincidence correction, and accommodating laser intensity fluctuations to enhance experimental efficiency and data accuracy.

## Contribution

It introduces a Bayesian approach that accounts for laser intensity fluctuations, enabling more accurate data analysis and higher ionization rates in PEPICO experiments.

## Key findings

- Bayesian method effectively corrects false coincidences.
- Laser fluctuations have minor impact on false coincidences.
- Background subtraction is significantly influenced by laser intensity variations.

## Abstract

This paper employs Bayesian probability theory for analyzing data generated in femtosecond pump-probe photoelectron-photoion coincidence (PEPICO) experiments. These experiments allow investigating ultrafast dynamical processes in photoexcited molecules. Bayesian probability theory is consistently applied to data analysis problems occurring in these types of experiments such as background subtraction and false coincidences. We previously demonstrated that the Bayesian formalism has many advantages, amongst which are compensation of false coincidences, no overestimation of pump-only contributions, significantly increased signal-to-noise ratio, and applicability to any experimental situation and noise statistics. Most importantly, by accounting for false coincidences, our approach allows running experiments at higher ionization rates, resulting in an appreciable reduction of data acquisition times. In addition to our previous paper, we include fluctuating laser intensities, of which the straightforward implementation highlights yet another advantage of the Bayesian formalism. Our method is thoroughly scrutinized by challenging mock data, where we find a minor impact of laser fluctuations on false coincidences, yet a noteworthy influence on background subtraction. We apply our algorithm to data obtained in experiments and discuss the impact of laser fluctuations on the data analysis.