Preferences help
enabled [disable] Abstract
Number of results
2017 | 132 | 3 | 967-969
Article title

An Artificial Neural Network-Based Estimation of Bremsstarahlung Photon Flux Calculated by MCNPX

Title variants
Languages of publication
Bremsstrahlung has an important place in the field of experimental physics, especially for description of photon-matter interaction and for characterization and analysis of materials. Bremsstrahlung photon is created by a high-energy electron, deflected in the electric field of atomic nucleus. Bremsstrahlung is also important for experimental studies, not only in the field of nuclear physics and particle physics but also in the fields of solid state physics, applied physics and astrophysics. In recent years, Monte Carlo simulation has become a widely used method for calculations related to bremsstrahlung. On the other hand, predictions by using artificial neural network can be performed with high accuracy. This study aims at observing variation in the photon flux as unction of target thickness and at processing output data by using an artificial neural network. We achieved a high degree of compatibility between two different methods. This study suggests that artificial neural network is a powerful tool for prediction of Bremsstrahlung and for other scientific problems.
  • Uskudar University, Vocational School of Health Services, Radiotherapy Department, İstanbul, Turkey
  • Usküdar University, Medical Radiation Research Center (USMERA), İstanbul, Turkey
  • Usküdar University, Medical Radiation Research Center (USMERA), İstanbul, Turkey
  • Usküdar University, Medical Radiation Research Center (USMERA), İstanbul, Turkey
  • Uskudar University, Vocational School of Health Service, Medical Imaging Department, Istanbul, Turkey
  • Uskudar University, Faculty of Engineering and Natural Sciences, Molecular Biology and Genetics Department, İstanbul, Turkey
  • Okan University, Faculity of Medicine, Department of Radiology, Istanbul, Turkey
  • [1] Y. Ozcanli, F. Kosovali Cavus, M. Beken, Acta Phys. Pol. A 130, 444 (2016), doi: 10.12693/APhysPolA.130.444
  • [2] M. Davraz, S. Kilincarslan, H. Ceylan, Acta Phys. Pol. A 128, B-184 (2015), doi: 10.12693/APhysPolA.128.B-184
  • [3] N. Zeng, Z. Wang, H. Zhang, F.E. Alsaadi, Cogn. Comput. 8, 143 (2016), doi: 10.1007/s12559-016-9396-6
  • [4] J. Liang, S.Y. Yuen, Cogn. Comput. 8, 693 (2016), doi: 10.1007/s12559-016-9406-8
  • [5] Z. Tang, J. Lu, P. Wang, Cogn. Comput. 7, 731 (2015), doi: 10.1007/s12559-015-9360-x
  • [6] I. Akkurt, K. Günoglu, H.O. Tekin, Z.N. Demirci, G. Yegin, N. Demir, Iranian J. Rad. Res. 10, 63 (2011)
  • [7] U. Kara, A. Mesbahi, I. Akkurt, Acta Phys. Pol. A 128, B-378 (2015), doi: 10.12693/APhysPolA.128.B-378
  • [8] N. Demir, Z.N. Kuluozturk, I. Akkurt, Acta Phys. Pol. A 128, B-443 (2015), doi: 10.12693/APhysPolA.128.B-443
  • [9] K. Gunoglu, N. Demir, I. Akkurt, Z.N. Demirci, Neural Comput. Applicat. 23, 1591 (2013)
  • [10] N. Demir, Z.N. Demirci, I. Akkurt, Radiat. Eff. Defects Solids 168, 372 (2013), doi: 10.1080/10420150.2013.777447
  • [11] J. Schmidhuber, Neural Networks 61, 85 (2015), doi: 10.1016/j.neunet.2014.09.003
  • [12] F. Amato, A. López, E. María, P. Méndez, P. Vaňhara, A. Hampl, J. Havel, J. Appl. Biomed. 11, 47 (2013), doi: 10.2478/v10136-012-0031-x
  • [13] RSICC Computer Code Collection, MCNPX User's Manual Version 2.4.0. Monte Carlo N-Particle Transport Code System for Multiple and High Energy Applications, 2002
  • [14] H.O. Tekin, Sci. Technol. Nucl. Installations 2016, 6547318 (2016), doi: 10.1155/2016/6547318
  • [15] I. Akkurt, H.O. Tekin, A. Mesbahi, Acta Phys. Pol. A 128, B-332 (2015), doi: 10.12693/APhysPolA.128.B-332
  • [16] H.O. Tekin, U. Kara, J. Communicat. Comput. 13, 32 (2016), doi: 10.17265/1548-7709/2016.01.005
  • [17] H.O. Tekin, T. Manici, C. Ekmekci, J. Health Sci. 4, 131 (2016), doi: 10.17265/2328-7136/2016.03.004
  • [18] H.O. Tekin, V.P. Singh, T. Manici, Appl. Radiat. Isotop. 121, 122 (2017), doi: 10.1016/j.apradiso.2016.12.040
  • [19] I. Akkurt, J.-O. Adler, J.R.M. Annand, F. Fasolo, K. Hansen, L. Isaksson, M. Karlsson, P. Lilja, M. Lundin, B. Nilsson, C. Ongaro, A. Reiter, G. Rosner, A. Sandell, B. Schröder, A. Zanini, Phys. Med. Biol. 48, 3345 (2003)
  • [20] O. Ritthoff, R. Klinkenberg, S. Fisher, I. Mierswa, S. Felske, YALE: Yet another Learning Environment. LLWA'01 - Tagungsband der GI-Workshop-Woche Lernen- Lehren - Wissen Adaptivitat, Technical Report 763, University of Dortmund, Dortmund 2001, p. 84
Document Type
Publication order reference
YADDA identifier
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.