首页> 外文会议>International conference on nuclear engineering;ICONE17 >OPTIMIZATION OF DEAD-LAYER THICKNESS FOR A HPGe DETECTOR USING UCODE-MCNP CODES
【24h】

OPTIMIZATION OF DEAD-LAYER THICKNESS FOR A HPGe DETECTOR USING UCODE-MCNP CODES

机译:使用UCODE-MCNP编码优化HPGe检测器的死层厚度

获取原文

摘要

The use of modeling programs to predict the response of HPGe detectors is increasing in importance due to the extensive laboratory work, both in term of source preparations and measuring time. MCNP code is a powerful and useful tool for the simulation of Ge-detector efficiency calibration. The experimental efficiency data and MCNP calculations based only on the known physical measurements of the HPGe crystal do not agree well in some detectors. Detector construction materials and surface dead layers must be well specified. The dead layer of Ge detector is one of the most important factors that affect the calculations. In addition, and if provided by the manufacturer, the dead layer may changes with time. Consequently, it is necessary to optimize the thickness of the detector's dead layer in order to obtain more accurate results for the efficiency of the detector using Monte Carlo calculations. Our approach consists of employing hybrid UCODE-MCNP codes to optimize the dead layer of the Ge-crystal aiming at decreasing discrepancies between experimental and simulated data of the Ge detector efficiency. UCODE has two attributes that are not jointly available in other inverse models: (1) the ability to work with any mathematically based model or pre- or post processor with ASCII or text only input and output files, and (2) the inclusion of more informative statistics.
机译:由于在源制备和测量时间方面都进行了大量的实验室工作,因此使用建模程序来预测HPGe检测器的响应的重要性日益增加。 MCNP代码是用于模拟Ge探测器效率校准的强大而有用的工具。在某些检测器中,仅基于HPGe晶体的已知物理测量结果的实验​​效率数据和MCNP计算结果并不一致。探测器的建筑材料和表面死层必须明确规定。锗探测器的死层是影响计算的最重要因素之一。另外,如果由制造商提供,则死层可能会随时间变化。因此,有必要优化检测器死层的厚度,以便使用蒙特卡洛计算获得更准确的检测器效率结果。我们的方法包括采用混合UCODE-MCNP代码来优化Ge晶体的死层,以减少Ge探测器效率的实验数据与模拟数据之间的差异。 UCODE具有两个在其他逆模型中无法共同使用的属性:(1)能够与任何基于数学的模型或具有ASCII或纯文本输入和输出文件的预处理器或后处理器一起使用,以及(2)包含更多属性信息丰富的统计信息。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号