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

Publikation: Beitrag in einer FachzeitschriftArtikelForschungBegutachtung

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.
Originalspracheenglisch
Aufsatznummer93
FachzeitschriftEntropy
Jahrgang21
Ausgabenummer1
DOIs
PublikationsstatusVeröffentlicht - 19 Jan 2019

Fingerprint

photoelectrons
pumps
subtraction
probes
formalism
lasers
data acquisition
signal to noise ratios
statistics
ionization
molecules

Schlagwörter

    ASJC Scopus subject areas

    • !!Physics and Astronomy(all)

    Dies zitieren

    @article{6b2a40c5052442e9b183d73aa9fcd53c,
    title = "Bayesian Analysis of Femtosecond Pump-Probe Photoelectron-Photoion Coincidence Spectra with Fluctuating Laser Intensities",
    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.",
    keywords = "Bayesian data analysis, Femtosecond pump-probe spectroscopy, PEPICO, Photoelectron-photoion coincidence, Ultrafast molecular dynamics",
    author = "Pascal Heim and Michael Rumetshofer and Sascha Ranftl and Bernhard Thaler and Ernst, {Wolfgang E.} and Markus Koch and {von der Linden}, Wolfgang",
    year = "2019",
    month = "1",
    day = "19",
    doi = "https://doi.org/10.3390/e21010093",
    language = "English",
    volume = "21",
    journal = "Entropy",
    issn = "1099-4300",
    publisher = "MDPI AG",
    number = "1",

    }

    TY - JOUR

    T1 - Bayesian Analysis of Femtosecond Pump-Probe Photoelectron-Photoion Coincidence Spectra with Fluctuating Laser Intensities

    AU - Heim, Pascal

    AU - Rumetshofer, Michael

    AU - Ranftl, Sascha

    AU - Thaler, Bernhard

    AU - Ernst, Wolfgang E.

    AU - Koch, Markus

    AU - von der Linden, Wolfgang

    PY - 2019/1/19

    Y1 - 2019/1/19

    N2 - 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.

    AB - 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.

    KW - Bayesian data analysis

    KW - Femtosecond pump-probe spectroscopy

    KW - PEPICO

    KW - Photoelectron-photoion coincidence

    KW - Ultrafast molecular dynamics

    UR - http://www.scopus.com/inward/record.url?scp=85060367973&partnerID=8YFLogxK

    U2 - https://doi.org/10.3390/e21010093

    DO - https://doi.org/10.3390/e21010093

    M3 - Article

    VL - 21

    JO - Entropy

    JF - Entropy

    SN - 1099-4300

    IS - 1

    M1 - 93

    ER -