English

\textsc{MaGe} - a {\sc Geant4}-based Monte Carlo Application Framework for Low-background Germanium Experiments

Nuclear Experiment 2011-07-06 v1

Abstract

We describe a physics simulation software framework, MAGE, that is based on the GEANT4 simulation toolkit. MAGE is used to simulate the response of ultra-low radioactive background radiation detectors to ionizing radiation, specifically the MAJORANA and GERDA neutrinoless double-beta decay experiments. MAJORANA and GERDA use high-purity germanium detectors to search for the neutrinoless double-beta decay of 76Ge, and MAGE is jointly developed between these two collaborations. The MAGE framework contains the geometry models of common objects, prototypes, test stands, and the actual experiments. It also implements customized event generators, GEANT4 physics lists, and output formats. All of these features are available as class libraries that are typically compiled into a single executable. The user selects the particular experimental setup implementation at run-time via macros. The combination of all these common classes into one framework reduces duplication of efforts, eases comparison between simulated data and experiment, and simplifies the addition of new detectors to be simulated. This paper focuses on the software framework, custom event generators, and physics lists.

Keywords

Cite

@article{arxiv.1011.3827,
  title  = {\textsc{MaGe} - a {\sc Geant4}-based Monte Carlo Application Framework for Low-background Germanium Experiments},
  author = {Melissa Boswell and Yuen-Dat Chan and Jason A. Detwiler and Padraic Finnerty and Reyco Henning and Victor M. Gehman and Rob A. Johnson and David V. Jordan and Kareem Kazkaz and Markus Knapp and Kevin Kröninger and Daniel Lenz and Lance Leviner and Jing Liu and Xiang Liu and Sean MacMullin and Michael G. Marino and Akbar Mokhtarani and Luciano Pandola and Alexis G. Schubert and Jens Schubert and Claudia Tomei and Oleksandr Volynets},
  journal= {arXiv preprint arXiv:1011.3827},
  year   = {2011}
}

Comments

12 pages, 6 figures

R2 v1 2026-06-21T16:44:51.083Z