scholarly journals Absolute Calibration of a Prompt Gamma-Ray Detector for Intense Bursts of Protons

1981 ◽  
Vol 9 (1) ◽  
pp. 24-29 ◽  
Author(s):  
F. C. Young ◽  
W. F. Oliphant ◽  
S. J. Stephanakis ◽  
A. R. Knudson
2010 ◽  
Vol 25 (3) ◽  
pp. 205-211 ◽  
Author(s):  
Hamed Panjeh ◽  
Hashem Hakimabad ◽  
Lalle Motavalli

The gamma ray spectrum resolution from a 241Am-Be source-based prompt gamma ray activation analysis set-up has been observed to increase in the energy region of interest with enclosing the NaI detector in a proper neutron and gamma ray shield. We have investigated the tact that the peak resolution of prompt gamma rays in the region of interest from the set-up depends on the source activity to the great extent, size and kind of the detector and the geometry of the detector shield. In order to see the role of a detector shield, five kinds of the detector shield were used and finally the proper kind was introduced. Since the detector shield has an important contribution in the reduction of the undesirable and high rate gamma rays coming to the gamma ray detector, a good design of a proper shield enables the elimination of the unwanted events, such as a pulse pile-up. By improving the shielding design, discrete and distinguishable photoelectric peaks in the energy region of interest have been observed in the spectrum of prompt gamma rays.


2014 ◽  
Vol 41 (6Part29) ◽  
pp. 495-495
Author(s):  
J Verburg ◽  
M Testa ◽  
E Cascio ◽  
T Bortfeld ◽  
H Lu ◽  
...  

1992 ◽  
Vol 63 (10) ◽  
pp. 4857-4859 ◽  
Author(s):  
S. S. Medley ◽  
A. L. Roquemore ◽  
F. E. Cecil

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

2013 ◽  
Vol 470 ◽  
pp. 012001 ◽  
Author(s):  
A Miceli ◽  
G Festa ◽  
R Senesi ◽  
G Gorini ◽  
C Andreani

2020 ◽  
Vol 152 ◽  
pp. S957-S958
Author(s):  
P. Costanza ◽  
R.I. Mackay ◽  
K.J. Kirkby ◽  
M.J. Taylor

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

2012 ◽  
Vol 8 (S290) ◽  
pp. 263-264
Author(s):  
Liang Li ◽  
En-Wei Liang ◽  
He Gao ◽  
Bing Zhang

AbstractWell-sampled optical lightcurves of 146 gamma-ray bursts (GRBs) are compiled from literature. We identify possible emission components based on our empirical fits and present statistical analysis for these components. We find that the flares are related to prompt emission, suggesting that they could have the same origin in different episodes. The shallow decay segment is not correlated with prompt gamma-rays. It likely signals a long-lasting injected wind from GRB central engines. Early after onset peak is closely related with prompt emission. The ambient medium density profile is likely n ∝ r−1. No correlation between the late re-brightening bump and prompt gamma-rays or the onset bump is found. They may be from another jet component.


Geophysics ◽  
1987 ◽  
Vol 52 (11) ◽  
pp. 1535-1546 ◽  
Author(s):  
Ping Sheng ◽  
Benjamin White ◽  
Balan Nair ◽  
Sandra Kerford

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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