Emulator-based Bayesian inference on non-proportional scintillation models by compton-edge probing

David Breitenmoser (), Francesco Cerutti, Gernot Butterweck, Malgorzata Magdalena Kasprzak and Sabine Mayer
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David Breitenmoser: Paul Scherrer Institute (PSI)
Francesco Cerutti: European Organization for Nuclear Research (CERN)
Gernot Butterweck: Paul Scherrer Institute (PSI)
Malgorzata Magdalena Kasprzak: Paul Scherrer Institute (PSI)
Sabine Mayer: Paul Scherrer Institute (PSI)

Nature Communications, 2023, vol. 14, issue 1, 1-12

Abstract: Abstract Scintillator detector response modeling has become an essential tool in various research fields such as particle and nuclear physics, astronomy or geophysics. Yet, due to the system complexity and the requirement for accurate electron response measurements, model inference and calibration remains a challenge. Here, we propose Compton edge probing to perform non-proportional scintillation model (NPSM) inference for inorganic scintillators. We use laboratory-based gamma-ray radiation measurements with a NaI(Tl) scintillator to perform Bayesian inference on a NPSM. Further, we apply machine learning to emulate the detector response obtained by Monte Carlo simulations. We show that the proposed methodology successfully constrains the NPSM and hereby quantifies the intrinsic resolution. Moreover, using the trained emulators, we can predict the spectral Compton edge dynamics as a function of the parameterized scintillation mechanisms. The presented framework offers a simple way to infer NPSMs for any inorganic scintillator without the need for additional electron response measurements.

Date: 2023
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DOI: 10.1038/s41467-023-42574-y

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