GEANT4 code for simulation of a germanium gamma-ray detector and its application to efficiency calibration

2004 ◽  
Vol 518 (3) ◽  
pp. 764-774 ◽  
Author(s):  
S. Hurtado ◽  
M. Garcı́a-León ◽  
R. Garcı́a-Tenorio
Keyword(s):  
Gamma Ray ◽  
Efficiency Calibration ◽  
Geant4 Code ◽  
Gamma Ray Detector

scholarly journals The STACEE ground-based gamma-ray detector

2005 ◽  
Author(s):  
D.M. Gingrich ◽  
L.M. Boone ◽  
D. Bramel ◽  
J. Carson ◽  
C.E. Covault ◽  
...  
Keyword(s):  
Gamma Ray ◽  
Gamma Ray Detector

scholarly journals Soft gamma-ray detector for the ASTRO-H Mission

2012 ◽  
Author(s):  
Shin Watanabe ◽  
Hiroyasu Tajima ◽  
Yasushi Fukazawa ◽  
Roger Blandford ◽  
Teruaki Enoto ◽  
...  
Keyword(s):  
Gamma Ray ◽  
Gamma Ray Detector

Bayesian deconvolution of gamma‐ray logs

Geophysics ◽  
1987 ◽  
Vol 52 (11) ◽  
pp. 1535-1546 ◽  
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.

scholarly journals First data with the Hybrid Array of Gamma Ray Detector (HAGRiD)

2018 ◽  
Vol 414 ◽  
pp. 190-194 ◽  
Author(s):  
K. Smith ◽  
T. Baugher ◽  
S. Burcher ◽  
A.B. Carter ◽  
J.A. Cizewski ◽  
...  
Keyword(s):  
Gamma Ray ◽  
Gamma Ray Detector

Development of Large-Area CdTe/n+-Si Epitaxial Layer-Based Heterojunction Diode-Type Gamma-Ray Detector Arrays

2018 ◽  
Vol 65 (4) ◽  
pp. 1066-1069 ◽  
Author(s):  
M. Niraula ◽  
K. Yasuda ◽  
M. Kojima ◽  
S. Kitagawa ◽  
S. Tsubota ◽  
...  
Keyword(s):  
Epitaxial Layer ◽  
Gamma Ray ◽  
Heterojunction Diode ◽  
Large Area ◽  
Detector Arrays ◽  
Gamma Ray Detector
Sign in / Sign up

Export Citation Format

Share Document