Advanced radiation detector development: Advanced semiconductor detector development: Development of a oom-temperature, gamma ray detector using gallium arsenide to develop an electrode detector

Cadmium zinc telluride (CZT) material is one of the preferred materials for the fabrication of X-ray and gamma-ray detector. In this paper, it is presented an experimental detector system based on pixellated CZT semiconductor detector. At the same time, some research and design on the surface signal-readout method and the preamplifier circuitry is made. The signal coming from the CdZnTe material exposed to the radiation through the experiment is successfully required. The collection-efficiency between the electron and the hole in the anode is test when the different bias is applied in the pixellated CZT semiconductor detector. The parameter of the CZT detector and validate the responsive effect for the radiation is evaluated.The experiments and evaluation basis for the development of subsequent gamma spectrometer.

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The STACEE ground-based gamma-ray detector

IEEE Symposium Conference Record Nuclear Science 2004. ◽

10.1109/nssmic.2004.1462067 ◽

2005 ◽

Author(s):

D.M. Gingrich ◽

L.M. Boone ◽

D. Bramel ◽

J. Carson ◽

C.E. Covault ◽

...

Keyword(s):

Gamma Ray ◽

Gamma Ray Detector

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Soft gamma-ray detector for the ASTRO-H Mission

10.1117/12.925977 ◽

2012 ◽

Cited By ~ 10

Author(s):

Shin Watanabe ◽

Hiroyasu Tajima ◽

Yasushi Fukazawa ◽

Roger Blandford ◽

Teruaki Enoto ◽

...

Keyword(s):

Gamma Ray ◽

Gamma Ray Detector

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Bayesian deconvolution of gamma‐ray logs

Geophysics ◽

10.1190/1.1442270 ◽

1987 ◽

Vol 52 (11) ◽

pp. 1535-1546 ◽

Cited By ~ 5

Author(s):

Ping Sheng ◽

Benjamin White ◽

Balan Nair ◽

Sandra Kerford

Keyword(s):

High Frequency ◽

Gamma Ray ◽

Simulated Data ◽

Field Measurements ◽

Bayesian Optimization ◽

Frequency Noise ◽

High Frequency Noise ◽

Data Application ◽

Gamma Ray Detector ◽

Gamma Ray Log

The spatial resolution of gamma‐ray logs is defined by the length 𝓁 of the gamma‐ray detector. To resolve thin beds whose thickness is less than 𝓁, it is generally desirable to deconvolve the data to reduce the averaging effect of the detector. However, inherent in the deconvolution operation is an amplification of high‐frequency noise, which can be a detriment to the intended goal of increased resolution. We propose a Bayesian statistical approach to gamma‐ray log deconvolution which is based on optimization of a probability function which takes into account the statistics of gamma‐ray log measurements as well as the empirical information derived from the data. Application of this method to simulated data and to field measurements shows that it is effective in suppressing high‐frequency noise encountered in the deconvolution of gamma‐ray logs. In particular, a comparison with the least‐squares deconvolution approach indicates that the incorporation of physical and statistical information in the Bayesian optimization process results in optimal filtering of the deconvolved results.

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