Balloon-flight test of a lanthanum bromide gamma-ray detector with silicon photomultiplier readout

Author(s):  
Peter F. Bloser ◽  
Jason S. Legere ◽  
Jonathan R. Wurtz ◽  
Luke F. Jablonski ◽  
Christopher M. Bancroft ◽  
...  
Author(s):  
David Murphy ◽  
Joseph Mangan ◽  
Alexei Ulyanov ◽  
Sarah Walsh ◽  
Rachel Dunwoody ◽  
...  

AbstractRecent advances in silicon photomultiplier (SiPM) technology and new scintillator materials allow for the creation of compact high-performance gamma-ray detectors which can be deployed on small low-cost satellites. A small number of such satellites can provide full sky coverage and complement, or in some cases replace the existing gamma-ray missions in detection of transient gamma-ray events. The aim of this study is to test gamma-ray detection using a novel commercially available CeBr3 scintillator combined with SiPM readout in a near-space environment and inform further technology development for a future space mission. A prototype gamma-ray detector was built using a CeBr3 scintillator and an array of 16 J-Series SiPMs by ON Semiconductor. SiPM readout was performed using SIPHRA, a radiation-tolerant low-power integrated circuit developed by IDEAS. The detector was flown as a piggyback payload on the Advanced Scintillator Compton Telescope balloon flight from Columbia Scientific Balloon Facility. The payload included the detector, a Raspberry Pi on-board computer, a custom power supply board, temperature and pressure sensors, a Global Navigation Satellite System receiver and a satellite modem. The balloon delivered the detector to 37 km altitude where its detection capabilities and readout were tested in the radiation-intense near-space environment. The detector demonstrated continuous operation during the 8-hour flight and after the landing. It performed spectral measurements in an energy range of 100 keV to 8 MeV and observed the 511 keV gamma-ray line arising from positron annihilation in the atmosphere with full width half maximum of 6.8%. During ascent and descent, the detector count rate peaked at an altitude of 16 km corresponding to the point of maximum radiation intensity in the atmosphere. Despite several engineering issues discovered after the flight test, the results of this study confirm the feasibility of using CeBr3 scintillator, SiPMs, and SIPHRA in future space missions.


2010 ◽  
Author(s):  
Camden Ertley ◽  
Christopher Bancroft ◽  
Peter Bloser ◽  
Taylor Connor ◽  
Jason Legere ◽  
...  

1990 ◽  
Author(s):  
Robert J. Hicks ◽  
David H. Jenkins

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

2012 ◽  
Vol 36 (4) ◽  
pp. 334-338 ◽  
Author(s):  
Fei Jia ◽  
Yong-Wei Dong ◽  
Jun-Ying Chai ◽  
Jiang-Tao Liu ◽  
Bo-Bing Wu ◽  
...  

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

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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