Production, characterization and operation of ${}^{76}$Ge enriched BEGe detectors in GERDA

Abstract

The GERmanium Detector Array (GERDA) at the Gran Sasso Underground Laboratory (LNGS) searches for the neutrinoless double beta decay (0νββ) of ${}^{76}$Ge. Germanium detectors made of material with an enriched ${}^{76}$Ge fraction act simultaneously as sources and detectors for this decay. During Phase I of the experiment mainly refurbished semi-coaxial Ge detectors from former experiments were used. For the upcoming Phase II, 30 new ${}^{76}$Ge enriched detectors of broad energy germanium (BEGe)-type were produced. A subgroup of these detectors has already been deployed in GERDA during Phase I. The present paper reviews the complete production chain of these BEGe detectors including isotopic enrichment, purification, crystal growth and diode production. The efforts in optimizing the mass yield and in minimizing the exposure of the ${}^{76}$Ge enriched germanium to cosmic radiation during processing are described. Furthermore, characterization measurements in vacuum cryostats of the first subgroup of seven BEGe detectors and their long-term behavior in liquid argon are discussed. The detector performance fulfills the requirements needed for the physics goals of GERDA Phase~II.

23 pages, 21 figures

References in corpus (2)

• The GERDA experiment for the search of 0νββ decay in ^{76}Ge
• \textsc{MaGe} - a {\sc Geant4}-based Monte Carlo Application Framework for Low-background Germanium Experiments

Cited by in corpus (6)

• The Processing of Enriched Germanium for the MAJORANA DEMONSTRATOR and R&D for a Possible Future Ton-Scale Ge-76 Double-Beta Decay Experiment
• Pulse shape analysis in GERDA Phase II
• Cosmogenic activation in double beta decay experiments
• Double Beta Decay Experiments at Canfranc Underground Laboratory
• Study of cosmogenic activation in ${}^{76}$Ge enriched germanium detectors during fabrication and transportation above ground
• Machine learning-powered data cleaning for LEGEND: a semi-supervised approach using affinity propagation and support vector machines