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تسجيل مستخدم جديدHighly sensitive gamma-spectrometers of GERDA for material screening: Part I
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D. Budjav{s}
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The GERDA experiment aims to search for the neutrinoless double beta-decay of 76Ge and possibly for other rare processes. The sensitivity of the first phase is envisioned to be more than one order of magnitude better than in previous neutrinoless double beta-decay experiments. This implies that materials with ultra-low radioactive contamination need to be used for the construction of the detector and its shielding. Therefore the requirements on material screening include high-sensitivity low-background detection techniques and long measurement times. In this article, an overview of material-screening laboratories available to the GERDA collaboration is given, with emphasis on the gamma-spectrometry. Additionally, results of an intercomparison of the evaluation accuracy in these laboratories are presented.
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The previous article about material screening for GERDA points out the importance of strict material screening and selection for radioimpurities as a key to meet the aspired background levels of the GERDA experiment. This is directly done using low-l
evel gamma-spectroscopy. In order to provide sufficient selective power in the mBq/kg range and below, the employed gamma-spectrometers themselves have to meet strict material requirements, and make use of an elaborate shielding system. This article gives an account of the setup of two such spectrometers. Corrado is located in a depth of 15 m w.e. at the MPI-K in Heidelberg (Germany), GeMPI III is situated at the Gran-Sasso underground laboratory at 3500 m w.e. (Italy). The latter one aims at detecting sample activities of the order ~0.01 mBq/kg, which is the current state-of-the-art level. The applied techniques to meet the respective needs are discussed and demonstrated by experimental results.
A gamma counting station based on high-purity germanium (HPGe) detector was set up for the material screening of the PandaX dark matter experiments in the China Jinping Underground Laboratory. Low background gamma rate of 2.6 counts/min within the en
ergy range of 20 to 2700 keV is achieved due to the well-designed passive shield. The sentivities of the HPGe detetector reach mBq/kg level for isotopes like K, U, Th, and even better for Co and Cs, resulted from the low-background rate and the high relative detection efficiency of 175%. The structure and performance of the counting station are described in this article. Detailed counting results for the radioactivity in materials used by the PandaX dark-matter experiment are presented. The upgrading plan of the counting station is also discussed.
The GERDA experiment located at the LNGS searches for neutrinoless double beta (0 ubetabeta) decay of ^{76}Ge using germanium diodes as source and detector. In Phase I of the experiment eight semi-coaxial and five BEGe type detectors have been deploy
ed. The latter type is used in this field of research for the first time. All detectors are made from material with enriched ^{76}Ge fraction. The experimental sensitivity can be improved by analyzing the pulse shape of the detector signals with the aim to reject background events. This paper documents the algorithms developed before the data of Phase I were unblinded. The double escape peak (DEP) and Compton edge events of 2.615 MeV gamma rays from ^{208}Tl decays as well as 2 ubetabeta decays of ^{76}Ge are used as proxies for 0 ubetabeta decay. For BEGe detectors the chosen selection is based on a single pulse shape parameter. It accepts 0.92$pm$0.02 of signal-like events while about 80% of the background events at Q_{betabeta}=2039 keV are rejected. For semi-coaxial detectors three analyses are developed. The one based on an artificial neural network is used for the search of 0 ubetabeta decay. It retains 90% of DEP events and rejects about half of the events around Q_{betabeta}. The 2 ubetabeta events have an efficiency of 0.85pm0.02 and the one for 0 ubetabeta decays is estimated to be 0.90^{+0.05}_{-0.09}. A second analysis uses a likelihood approach trained on Compton edge events. The third approach uses two pulse shape parameters. The latter two methods confirm the classification of the neural network since about 90% of the data events rejected by the neural network are also removed by both of them. In general, the selection efficiency extracted from DEP events agrees well with those determined from Compton edge events or from 2 ubetabeta decays.
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