Detector signal characterization with a Bayesian network in XENONnT

DOI DOI DOI HANDLE HANDLE View 5 Remaining Hide 31 References Open Access

Search this article

Description

We developed a detector signal characterization model based on a Bayesian network trained on the waveform attributes generated by a dual-phase xenon time projection chamber. By performing inference on the model, we produced a quantitative metric of signal characterization and demonstrate that this metric can be used to determine whether a detector signal is sourced from a scintillation or an ionization process. We describe the method and its performance on electronic-recoil (ER) data taken during the first science run of the XENONnT dark matter experiment. We demonstrate the first use of a Bayesian network in a waveform-based analysis of detector signals. This method resulted in a 3% increase in ER event-selection efficiency with a simultaneously effective rejection of events outside of the region of interest. The findings of this analysis are consistent with the previous analysis from XENONnT, namely a background-only fit of the ER data.

Journal

  • Physical Review D

    Physical Review D 108 (1), 2023-07-26

    American Physical Society (APS)

References(31)*help

See more

Related Projects

See more

Keywords

Report a problem

Back to top