skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Numerical integration of detector response functions via Monte Carlo simulations

Abstract

Calculations of detector response functions are complicated because they include the intricacies of signal creation from the detector itself as well as a complex interplay between the detector, the particle-emitting target, and the entire experimental environment. As such, these functions are typically only accessible through time-consuming Monte Carlo simulations. Furthermore, the output of thousands of Monte Carlo simulations can be necessary in order to extract a physics result from a single experiment. Here we describe a method to obtain a full description of the detector response function using Monte Carlo simulations. We also show that a response function calculated in this way can be used to create Monte Carlo simulation output spectra a factor of ~1000× faster than running a new Monte Carlo simulation. A detailed discussion of the proper treatment of uncertainties when using this and other similar methods is provided as well. Here, this method is demonstrated and tested using simulated data from the Chi-Nu experiment, which measures prompt fission neutron spectra at the Los Alamos Neutron Science Center.

Authors:
 [1];  [1];  [1];  [1]; ORCiD logo [1];  [1];  [1]; ORCiD logo [1];  [1];  [2];  [2]; ORCiD logo [2]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  2. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1364576
Report Number(s):
LA-UR-17-22123
Journal ID: ISSN 0168-9002; TRN: US1702526
Grant/Contract Number:
AC52-06NA25396
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Nuclear Instruments and Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment
Additional Journal Information:
Journal Volume: 866; Journal ID: ISSN 0168-9002
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY; Detector Response, MCNP, lithium-glass detector

Citation Formats

Kelly, Keegan John, O'Donnell, John M., Gomez, Jaime A., Taddeucci, Terry Nicholas, Devlin, Matthew James, Haight, Robert Cameron, White, Morgan Curtis, Mosby, Shea Morgan, Neudecker, D., Buckner, Matthew Quinn, Wu, Ching -Yen, and Lee, Hye Young. Numerical integration of detector response functions via Monte Carlo simulations. United States: N. p., 2017. Web. doi:10.1016/j.nima.2017.05.048.
Kelly, Keegan John, O'Donnell, John M., Gomez, Jaime A., Taddeucci, Terry Nicholas, Devlin, Matthew James, Haight, Robert Cameron, White, Morgan Curtis, Mosby, Shea Morgan, Neudecker, D., Buckner, Matthew Quinn, Wu, Ching -Yen, & Lee, Hye Young. Numerical integration of detector response functions via Monte Carlo simulations. United States. doi:10.1016/j.nima.2017.05.048.
Kelly, Keegan John, O'Donnell, John M., Gomez, Jaime A., Taddeucci, Terry Nicholas, Devlin, Matthew James, Haight, Robert Cameron, White, Morgan Curtis, Mosby, Shea Morgan, Neudecker, D., Buckner, Matthew Quinn, Wu, Ching -Yen, and Lee, Hye Young. 2017. "Numerical integration of detector response functions via Monte Carlo simulations". United States. doi:10.1016/j.nima.2017.05.048.
@article{osti_1364576,
title = {Numerical integration of detector response functions via Monte Carlo simulations},
author = {Kelly, Keegan John and O'Donnell, John M. and Gomez, Jaime A. and Taddeucci, Terry Nicholas and Devlin, Matthew James and Haight, Robert Cameron and White, Morgan Curtis and Mosby, Shea Morgan and Neudecker, D. and Buckner, Matthew Quinn and Wu, Ching -Yen and Lee, Hye Young},
abstractNote = {Calculations of detector response functions are complicated because they include the intricacies of signal creation from the detector itself as well as a complex interplay between the detector, the particle-emitting target, and the entire experimental environment. As such, these functions are typically only accessible through time-consuming Monte Carlo simulations. Furthermore, the output of thousands of Monte Carlo simulations can be necessary in order to extract a physics result from a single experiment. Here we describe a method to obtain a full description of the detector response function using Monte Carlo simulations. We also show that a response function calculated in this way can be used to create Monte Carlo simulation output spectra a factor of ~1000× faster than running a new Monte Carlo simulation. A detailed discussion of the proper treatment of uncertainties when using this and other similar methods is provided as well. Here, this method is demonstrated and tested using simulated data from the Chi-Nu experiment, which measures prompt fission neutron spectra at the Los Alamos Neutron Science Center.},
doi = {10.1016/j.nima.2017.05.048},
journal = {Nuclear Instruments and Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment},
number = ,
volume = 866,
place = {United States},
year = 2017,
month = 6
}

Journal Article:
Free Publicly Available Full Text
This content will become publicly available on June 13, 2018
Publisher's Version of Record

Save / Share:
  • A methodology for obtaining empirical curves relating absolute measured scintillation light output to beta energy deposited is presented. Output signals were measured from thin plastic scintillator using NIST traceable beta and gamma sources and MCNP5 was used to model the energy deposition from each source. Combining the experimental and calculated results gives the desired empirical relationships. To validate, the sensitivity of a beryllium/scintillator-layer neutron activation detector was predicted and then exposed to a known neutron fluence from a Deuterium-Deuterium fusion plasma (DD). The predicted and the measured sensitivity were in statistical agreement.
  • Monte Carlo simulation techniques are derived for solving the ordinary Langevin equation of motion for a Brownian particle in the presence of an external force. These methods allow considerable freedom in selecting the size of the time step, which is restricted only by the rate of change in the external force. This approach is extended to the generalized Langevin equation which uses a memory function in the friction force term. General simulation techniques are derived which are independent of the form of the memory function. A special method requiring less storage space is presented for the case of the exponentialmore » memory function.« less
  • In this paper, we develop an improved multilevel Monte Carlo (MLMC) method for estimating cumulative distribution functions (CDFs) of a quantity of interest, coming from numerical approximation of large-scale stochastic subsurface simulations. Compared with Monte Carlo (MC) methods, that require a significantly large number of high-fidelity model executions to achieve a prescribed accuracy when computing statistical expectations, MLMC methods were originally proposed to significantly reduce the computational cost with the use of multifidelity approximations. The improved performance of the MLMC methods depends strongly on the decay of the variance of the integrand as the level increases. However, the main challengemore » in estimating CDFs is that the integrand is a discontinuous indicator function whose variance decays slowly. To address this difficult task, we approximate the integrand using a smoothing function that accelerates the decay of the variance. In addition, we design a novel a posteriori optimization strategy to calibrate the smoothing function, so as to balance the computational gain and the approximation error. The combined proposed techniques are integrated into a very general and practical algorithm that can be applied to a wide range of subsurface problems for high-dimensional uncertainty quantification, such as a fine-grid oil reservoir model considered in this effort. The numerical results reveal that with the use of the calibrated smoothing function, the improved MLMC technique significantly reduces the computational complexity compared to the standard MC approach. Finally, we discuss several factors that affect the performance of the MLMC method and provide guidance for effective and efficient usage in practice.« less
  • We develop an improved multilevel Monte Carlo (MLMC) method for estimating cumulative distribution functions (CDFs) of a quantity of interest, coming from numerical approximation of large-scale stochastic subsurface simulations. Compared with Monte Carlo (MC) methods, that require a significantly large number of high-fidelity model executions to achieve a prescribed accuracy when computing statistical expectations, MLMC methods were originally proposed to significantly reduce the computational cost with the use of multifidelity approximations. The improved performance of the MLMC methods depends strongly on the decay of the variance of the integrand as the level increases. However, the main challenge in estimating CDFsmore » is that the integrand is a discontinuous indicator function whose variance decays slowly. To address this difficult task, we approximate the integrand using a smoothing function that accelerates the decay of the variance. Additionally, we design a novel a posteriori optimization strategy to calibrate the smoothing function, so as to balance the computational gain and the approximation error. The combined proposed techniques are integrated into a very general and practical algorithm that can be applied to a wide range of subsurface problems for high-dimensional uncertainty quantification, such as a fine-grid oil reservoir model considered in this effort. Furthermore, the numerical results reveal that with the use of the calibrated smoothing function, the improved MLMC technique significantly reduces the computational complexity compared to the standard MC approach. Finally, we discuss several factors that affect the performance of the MLMC method and provide guidance for effective and efficient usage in practice.« less
  • The recently developed Nambu-Jona-Lasinio--jet model is used as an effective chiral quark theory to calculate the quark fragmentation functions to pions, kaons, nucleons, and antinucleons. The effects of the vector mesons {rho}, K{sup *}, and {phi} on the production of secondary pions and kaons are included. The fragmentation processes to nucleons and antinucleons are described by using the quark-diquark picture, which has been shown to give a reasonable description of quark distribution functions. We incorporate effects of next-to-leading order in the Q{sup 2} evolution, and compare our results with the empirical fragmentation functions.