设计和研制了一种简便的宇宙线μ子寿命测量的实验装置.该装置采用大面积闪烁体探测器,利用可编程逻辑阵列(field program mable gatearray,FPGA),并通过电子学脉冲计数法,实现对宇宙线μ子的衰变寿命测量.测量结果与文献中给出μ子寿...设计和研制了一种简便的宇宙线μ子寿命测量的实验装置.该装置采用大面积闪烁体探测器,利用可编程逻辑阵列(field program mable gatearray,FPGA),并通过电子学脉冲计数法,实现对宇宙线μ子的衰变寿命测量.测量结果与文献中给出μ子寿命精确值比较,误差小于3.5%.该实验原理及测量方法简单,测量条件要求不高,可用于大学物理实验教学.相关实验方法也可应用到其他一些粒子寿命及时间测量的实验中.展开更多
To solve the problems associated with low resolution and high computational effort infinite time,this paper proposes a fast forward modeling method for muon energy loss transmission tomography based on a model voxeliza...To solve the problems associated with low resolution and high computational effort infinite time,this paper proposes a fast forward modeling method for muon energy loss transmission tomography based on a model voxelization energy loss projection algorithm.First,the energy loss equation for muon transmission tomography is derived from the Bethe–Bloch formula,and the imaging region is then dissected into several units using the model voxelization method.Thereafter,the three-dimensional(3-D)imaging model is discretized into parallel and equally spaced two-dimensional(2-D)slices using the model layering method to realize a dimensional reduction of the 3-D volume data and accelerate the forward calculation speed.Subsequently,the muon energy loss transmission tomography equation is discretized using the ray energy loss projection method to establish a set of energy loss equations for the muon penetration voxel model.Finally,the muon energy loss values at the outgoing point are obtained by solving the projection coefficient matrix of the ray length-weighted model,achieving a significant reduction in the number of muons and improving the computational efficiency.A comparison of our results with the simulation results based on the Monte Carlo method verifies the accuracy and effectiveness of the algorithm proposed in this paper.The metallic mineral identification tests show that the proposed algorithm can quickly identify high-density metallic minerals.The muon energy loss response can accurately identify the boundary of the anomalies and their spatial distribution characteristics.展开更多
文摘设计和研制了一种简便的宇宙线μ子寿命测量的实验装置.该装置采用大面积闪烁体探测器,利用可编程逻辑阵列(field program mable gatearray,FPGA),并通过电子学脉冲计数法,实现对宇宙线μ子的衰变寿命测量.测量结果与文献中给出μ子寿命精确值比较,误差小于3.5%.该实验原理及测量方法简单,测量条件要求不高,可用于大学物理实验教学.相关实验方法也可应用到其他一些粒子寿命及时间测量的实验中.
基金supported by the National Key Research and Development Project of China (2016YFC0303104)the National Natural Science Foundation of China(41304090)。
文摘To solve the problems associated with low resolution and high computational effort infinite time,this paper proposes a fast forward modeling method for muon energy loss transmission tomography based on a model voxelization energy loss projection algorithm.First,the energy loss equation for muon transmission tomography is derived from the Bethe–Bloch formula,and the imaging region is then dissected into several units using the model voxelization method.Thereafter,the three-dimensional(3-D)imaging model is discretized into parallel and equally spaced two-dimensional(2-D)slices using the model layering method to realize a dimensional reduction of the 3-D volume data and accelerate the forward calculation speed.Subsequently,the muon energy loss transmission tomography equation is discretized using the ray energy loss projection method to establish a set of energy loss equations for the muon penetration voxel model.Finally,the muon energy loss values at the outgoing point are obtained by solving the projection coefficient matrix of the ray length-weighted model,achieving a significant reduction in the number of muons and improving the computational efficiency.A comparison of our results with the simulation results based on the Monte Carlo method verifies the accuracy and effectiveness of the algorithm proposed in this paper.The metallic mineral identification tests show that the proposed algorithm can quickly identify high-density metallic minerals.The muon energy loss response can accurately identify the boundary of the anomalies and their spatial distribution characteristics.
