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Influence of Geant4 Physics List on Simulation Accuracy and Performance

by Róbert Breier, Alexander Fuss, Holger Kluck, Valentyna Mokina, Veronika Palušová, Pavel Povinec

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Submission summary

Authors (as registered SciPost users): Valentyna Mokina · Veronika Palusova
Submission information
Preprint Link: scipost_202209_00048v3  (pdf)
Date accepted: 2023-03-07
Date submitted: 2022-12-13 13:01
Submitted by: Palusova, Veronika
Submitted to: SciPost Physics Proceedings
Proceedings issue: 14th International Conference on Identification of Dark Matter (IDM2022)
Ontological classification
Academic field: Physics
Specialties:
  • Nuclear Physics - Experiment
Approaches: Experimental, Computational

Abstract

A main goal of current low background physics is the search for rare and novel phenomena beyond the Standard Model of particle physics, e.g. the scattering off of a potential Dark Matter particle inside a CaWO4 crystal or the neutrinoless double beta decay of Ge nucleus. The success of such searches depends on a reliable background prediction via Monte Carlo simulations. A widely used toolkit to construct these simulations is GEANT4, which offers a wide choice of physics models, so-called physics lists. To facilitate the selection of physics lists for simulations of CaWO4 and Ge targets, we quantify their impact on the total energy deposition for several test cases.

List of changes

- GoF abbreviation has been changed to goodness-of-fit in the conclusion section.
- A sentence has been added to text of Section 3 and captions of figures 2 and 3 to say explicitly that the efficiency and run time is calculated and plotted regardless of the size of the detector. A separate analysis and statements about targets (CaWO4 vs Ge) and target thickness (thin vs bulky) will be a part of the upcoming publication with the final results.

Published as SciPost Phys. Proc. 12, 063 (2023)


Reports on this Submission

Anonymous Report 1 on 2022-12-14 (Invited Report)

Strengths

I am happy with the changes.

Weaknesses

N/A.

Report

Recommend to publish

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