Production threshold impact on a GEANT4 calculation of the power deposition in a fast domain: MEGAPIE spallation target

  • Abdesslam LamrabetEmail author
  • Abdelmajid Maghnouj
  • Jaouad Tajmouati
  • Mohamed Bencheikh


The calculation time in the Monte Carlo simulations consistently represents an essential issue. It is often very long, and its decrease constitutes a challenge for the simulator. Generally, an MC simulation is qualified as quality or not according to two main criteria: the calculation time and the accuracy of the results. However, in most cases, the optimization of one criterion affects negatively the other. Therefore, a compromise between both of them is always required in this kind of simulation. The present work aims at studying the impact of the production threshold (or cut) of the GEANT4 toolkit on the calculation of the power deposition in the MEGAPIE spallation target. The production threshold of secondaries is a GEANT4 intrinsic parameter. It indicates the limit of energy we can reach in the production of secondary particles. This study has allowed us to make the following conclusions. First, the influence of the cut on the calculation of the deposited power depends on the volume size, its arrangement and the importance of the electromagnetic processes occurring within. Second, the accuracy of the calculations can be acceptable only below a given value of the cut energy. Third, this accuracy remains almost unchangeable from a certain value of the cut. The study has also made it possible to explore the prevalence of certain interactions in the zone of spallation in the MEGAPIE target.


Production threshold Power deposition Spallation MEGAPIE GEANT4 



The calculations of this simulation are carried out in the national grid of calculation “MaGrid” managed by the national center of scientific and technological research CNRST in Morocco. The authors of this work are grateful to all the staff of MaGrid.


  1. 1.
    H. Nikjoo, S. Uehara, D. Emfietzoglou et al., Track-structure codes in radiation research. Radiat. Meas. 41, 1052 (2006). CrossRefGoogle Scholar
  2. 2.
    L.S. Waters et al., MCNPX Users’s Manual Version 2.4.0, LA-CP-02-408 (Los Alamos National Laboratory, New Mexico, 2002)Google Scholar
  3. 3.
    M.J. Berger, Monte Carlo calculation of the penetration and diffusion of fast charged particles. Methods Comput. Phys. 1, 135 (1963)Google Scholar
  4. 4.
    T.M. Jenkins, W.R. Nelson, A. Rindi (eds.), Monte Carlo Transport of Electrons and Photons (Springer, Boston, 1988). CrossRefGoogle Scholar
  5. 5.
    A. Kling, F.J.C. Baräo, M. Nakagawa, et al. (eds.), Advanced Monte Carlo for Radiation Physics, Particle Transport Simulation and Applications. Proc. MC (Springer, Berlin, 2000). CrossRefGoogle Scholar
  6. 6.
    Y. Richet, O. Jacquet, X. Bay, Initialization Bias Suppression of an Iterative Monte Carlo Calculation. Am. Nucl. Soc. (2005). hal: 00409762v1Google Scholar
  7. 7.
    F. Salvat, J.M. Fernández-Varea, J. Sempau et al., Practical aspects of Monte Carlo simulation of charged particle transport: mixed algorithms and variance reduction techniques. Rad. Environ. Biophys. 38, 15–22 (1999). CrossRefGoogle Scholar
  8. 8.
    S. Agostinelli, J. Allison, K. Amako et al., GEANT4—a simulation toolkit. Nucl. Instrum. Methods A 506, 250–303 (2003). CrossRefGoogle Scholar
  9. 9.
    J. Allison, K. Amako, J. Apostolakis et al., GEANT4 developments and applications. IEEE. Trans. Nucl. Sci. 53, 270–278 (2006). CrossRefGoogle Scholar
  10. 10.
    J. Apostolakis, M. Asai, A.G. Bogdanov et al., Geometry and physics of the GEANT4 toolkit for high and medium energy applications. Radiat. Phys. Chem. 78, 859–873 (2009). CrossRefGoogle Scholar
  11. 11.
  12. 12.
    C.Latge, F.Groeschel, P. Agostini, et al., Megapie spallation target: design, implementation and preliminary tests of the first prototypical spallation target for future ADS. HAL: in2p3-00290482v1Google Scholar
  13. 13.
    G.S. Bauer, M. Salvatores, G. Heusener, MEGAPIE a 1 MW pilot experiment for a liquid metal spallation target. J. Nucl. Mater. 296, 17 (2001). CrossRefGoogle Scholar
  14. 14.
    A. Lamrabet, A. Maghnouj, J. Tajmouati et al., Assessment of the power deposition on the MEGAPIE spallation target using the GEANT4 toolkit. Nucl. Sci. Technol. 30, 54 (2019). CrossRefGoogle Scholar
  15. 15.
    G. Santin, D. Strul, D. Lazaro et al., GATE: a GEANT4-based simulation platform for PET and SPECT integrating movement and time management. IEEE. Trans. Nucl. Sci. 50, 1516–1521 (2003). CrossRefGoogle Scholar
  16. 16.
    D. Strulab, G. Santin, D. Lazaro et al., GATE (geant4 application for tomographic emission): a PET/SPECT general-purpose simulation platform. Nucl. Phys. B 125, 75–79 (2003). CrossRefGoogle Scholar
  17. 17.
    GAMOS. Accessed 6 Apr 2019
  18. 18.
    P. Arce, J.I. Lagares, L.J. Harkness-Brennan et al., Gamos: a framework to do Geant4 simulations in different physics fields with an user-friendly interface. Nucl. Instrum. Methods A 735, 304–313 (2014). CrossRefGoogle Scholar
  19. 19.
    Y. Malyshkin, I. Pshenichnov, I. Mishustin et al., Neutron production and energy deposition in fissile spallation targets studied with GEANT4 toolkit. Nucl. Instrum. Methods B 289, 79–90 (2012). CrossRefGoogle Scholar
  20. 20.
    Y. Malyshkin et al., Modeling spallation reactions in tungsten and uranium targets with the GEANT4 toolkit. EPJ Web Conf. 21, 10006 (2012). CrossRefGoogle Scholar
  21. 21.
    Y. Malyshkin, I. Pshenichnov, I. Mishustin et al., Monte Carlo modeling of spallation targets containing uranium and americium. Nucl. Instrum. Methods B 334, 8–17 (2014). CrossRefGoogle Scholar
  22. 22.
    I. Mishustin,Y. Malyshkin, I. Pshenichnov, et al., Possible production of neutron-rich heavy nuclei in fissile spallation targets, in Nuclear Physics: Present and Future, ed. by W. Greiner (2015), pp. 151–161. Google Scholar
  23. 23.
    J. el Bakkali, T. El Bardouni, Validation of MC GEANT4 code for a 6 MV Varian linac. J. King Saud Univ. Sci. 29, 106 (2017). CrossRefGoogle Scholar
  24. 24.
    B. Schmidt, J. González-Domínguez, C. Hundt, et al., C++11 Multithreading, Parallel Programming, Chap 4, pp. 77–133 (2018). CrossRefGoogle Scholar
  25. 25. Accessed 6 Apr 2019
  26. 26.
    CLHEP—A Class Library for High Energy Physics. Accessed 6 Apr 2019
  27. 27.
    ROOT a Data analysis Framework. Accessed 6 Apr 2019
  28. 28.
    Qt | Cross-platform software development for embedded & desktop. Accessed 6 Apr 2019
  29. 29.
    A. Lamrabet, A. Maghnouj, J. Tajmouati, GEANT4 modeling of the international MEGAPIE experiment. Adv. Stud. Theor. Phys. 11, 567 (2017). CrossRefGoogle Scholar
  30. 30.
    D. Wright, Geant4 Tutorial at M.D. Anderson Cancer Center. (2018). Accessed 19 Aug 2018
  31. 31.
    N. Zahra, T. Frisson, L. Grevillot et al., Influence of Geant4 parameters on dose distribution and computation time for carbon ion therapy simulation. Physica Med. 26, 202–208 (2010). CrossRefGoogle Scholar
  32. 32.
    S. Jan, D. Benoit, E. Becheva et al., GATE V6: a major enhancement of the GATE simulation platform enabling modelling of CT and radiotherapy. Phys. Med. Biol. 56, 881 (2011). CrossRefGoogle Scholar
  33. 33.
    K. Kurosu, M. Takashina, M. Koizumi et al., Optimization of GATE and PHITS Monte Carlo code parameters for uniform scanning proton beam based on simulation with FLUKA general-purpose code. Nucl. Instrum. Methods B 336, 45–54 (2014). CrossRefGoogle Scholar
  34. 34.
    D. Patel, L. Bronk, F. Guan et al., Optimization of Monte Carlo particle transport parameters and validation of a novel high throughput experimental setup to measure the biological effects of particle beams. Med. Phys. 44, 6061 (2017). CrossRefGoogle Scholar
  35. 35.
    D. Filges, F. Goldenbaum, Handbook of Spallation Research: Theory, Experiments and Applications (Wiley, New York, 2010)Google Scholar
  36. 36.
    C. Patrignani, K. Agashe, G. Aielli et al., Particle data group. Chin. Phys. C 40, 100001 (2016). CrossRefGoogle Scholar

Copyright information

© China Science Publishing & Media Ltd. (Science Press), Shanghai Institute of Applied Physics, the Chinese Academy of Sciences, Chinese Nuclear Society and Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Abdesslam Lamrabet
    • 1
    • 2
    Email author
  • Abdelmajid Maghnouj
    • 1
  • Jaouad Tajmouati
    • 1
  • Mohamed Bencheikh
    • 1
  1. 1.Laboratory of Integration of Systems and Advanced Technologies, Faculty of Sciences Dhar El mahrazUniversity Sidi Mohamed Ben AbdellahFèsMorocco
  2. 2.El HajebMorocco

Personalised recommendations