Araştırma Makalesi
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Prediction of Muon Energy using Deep Neural Network with Multiple Coulomb Scattering Data

Yıl 2022, , 975 - 987, 30.09.2022
https://doi.org/10.31202/ecjse.1017848

Öz

This study is based on the determination of muon beam energies using multiple Coulomb scattering data in artificial neural networks. Muon particles were scattered off a 50-layer lead object by using the G4beamline simulation program which is based on Geant4. Before working with deep neural networks, average scattering angle distributions in terms of the number of crossed layers were analyzed with the fitting method using the well-known formula for multiple Coulomb scattering to estimate muon beam energies. Subsequently, average scattering angles over the number of crossed layers from 1 to 10 were used in deep neural network structures to estimate the muon beam energy. It has been observed that deep neural networks significantly improve the resolutions compared to the ones obtained with the fitting method.

Kaynakça

  • Tao W.M. et al. (Particle Data Group), “Review of Particle Physics”, J. Phys. G: Nucl. Part. Phys, 2006, 33(1):1-1232.
  • Moliere G.V., “Theorie der Streuung schneller geladener Teilchen I”, Z. Naturforschg A, 1947, 2a:133-145; Moliere G.V., “Theorie der Streuung schneller geladener Teilchen II”, Z. Naturforschg A. 1948, 3a:78-97.
  • Bethe H.A., “Molière's Theory of Multiple Scattering”, Phys. Rev. 1953, 89:1256.
  • Olbert S., “Application of the Multiple Scattering Theory to Cloud-Chamber Measurements I”, Phys. Rev, 1952, 87:319.
  • Annis M., Bridge H.S., Olbert S., “Application of the Multiple Scattering Theory to Cloud-Chamber Measurements II”, Phys. Rev., 1953, 89:1216.
  • Voyvodic L., Pickup E., “Multiple Scattering of Fast Particles in Photographic Emulsions”, Phys. Rev., 1952, 85:91.
  • Pinkau K., “Moliere's Theory of Multiple Scattering Applied to the Spark Chamber”, Z. Phys., 1966, 196(2):163-173.
  • Ambrosio M., Antolini R., Auriemma G., Bakari D., Baldini A., Barbarino G.C. et al., “Muon energy estimate through multiple scattering with the MACRO detector”, Nuclear Instruments and Methods in Physics Research Section A, 2002, 492(3): 376-386.
  • Ambrosio M., Antolini R., Bakari D., Baldini A., Barbarino G.C., Barish B.C. et al., “Atmospheric neutrino oscillations from upward throughgoing muon multiple scattering in MACRO”, Phys. Lett. B, 2003, 566(1-2): 35-44.
  • Ankowski A., Antonello M., Aprili P., Arneodo F., Badertscher A., Baiboussinov B. et al., “Measurement of through-going particle momentum by means of multiple scattering with the ICARUS T600 TPC”, Eur. Phys. J C, 2006, 48:667-676.
  • Borozdin K.N., Hogan G.E., Morris C., Priedhorsky C., Saunders A., Schultz L.J. et al., “Radiographic imaging with cosmic-ray muons”, Nature, 2003, 422: 277.
  • Pesente S., Vanini S., Benettoni M., Bonomi G., Calvini P., Checciha P. et al., “First results on material identification and imaging with a large-volume muon tomography prototype”, Nuclear Instruments and Methods in Physics Research Section A, 2009, 604(3):738-746.
  • Agafonova N., Aleksandrov A., Altinok O., Anokhina A., Aoki S., Ariga A. et al., “Momentum measurement by the multiple Coulomb scattering method in the OPERA lead-emulsion target”, New Journal of Physics, 2012, 14:013026 (19 pp).
  • Keskenler M., Dal, D., Aydin, T., “Yapay Zeka Destekli ÇOKS Yöntemi ile Kredi Kartı Sahtekarlığının Tespiti", El-Cezeri, 2021, 8(2):1007-1023.
  • Aylak B., Oral O., Yazıcı K., "Yapay Zeka ve Makine Öğrenmesi Tekniklerinin Lojistik Sektöründe Kullanımı", El-Cezeri, 2021, 8(1):74-93.
  • Çınar S., Bünyan Ünel F., "2/B orman vasfını yitirmiş araziden tarım arazisine dönüşen taşınmazların toplu değerlemesi", Geomatik, 2022, 7(2): 112-127.
  • Demiryege İ., Ulukavak M., "Derin öğrenme tabanlı iyonosferik TEC tahmini", Geomatik, 2022, 7(2): 80-87.
  • Öztürk A., Allahverdı N., Saday F., "Application of artificial intelligence methods for bovine gender prediction", Turkish Journal of Engineering, 2022, 6(1): 54-62.
  • Sertkaya C., Akçay S., "Giysi Endüstrisinde Üretim Performansının Tahmininde Yapay Sinir Ağlarının Kullanılması", Avrupa Bilim ve Teknoloji Dergisi, 2021, (28): 34-39.
  • Gemirter. C. B., Goularas D., "A Turkish Question Answering System Based on Deep Learning Neural Networks", Journal of Intelligent Systems: Theory and Applications, 2021 4(2): 65-75.
  • Darendeli B. N., Yılmaz A., "Convolutional Neural Network Approach to Predict Tumor Samples Using Gene Expression Data", Journal of Intelligent Systems: Theory and Applications, 2021, 4(2): 136-141.
  • Kamber E., Körpüz S., Can M., Yumurtacı Aydoğmuş H., Gümüş M., “Yapay Sinir Ağlarina Dayali Kısa Dönemli Elektrik Yükü Tahmini”, Endüstri Mühendisliği, 2021, 32 (2):364-379.
  • Grieder P.K.F., “Cosmic Rays at Earth”, 1st ed., Elsevier Science, Amsterdam (2001).
  • Hohlmann M., Ford P., Gnanvo K., Helsby J., Pena D., Hoch R., et al., “GEANT4 simulation of a cosmic ray muon tomography system with micropattern gas detectors for the detection of High Z materials”, IEEE Trans. Nulc Sci., 2009, 56(3):1356–1363.
  • Priedhorsky W.C., Borozdin K.N., Hogan G.E., Morris C., Saunders A., Schultz L.J. et al., “Detection of high-Z objects using multiple scattering of cosmic ray muons”, Review of Scientific Instruments, 2003, 74:4294.
  • Schultz L.J., Borozdin K.N., Gomez J.J., Hogan G.E., McGill J.A., Morris C.L. et al., “Image reconstruction and material discrimination via cosmic ray muon radiography”, Nuclear Instruments and Methods in Physics Research Section A, 2004, 519(3):687–694.
  • Schultz J.L., Blanpied G.S., Borozdin K.N., Fraser A.M., Hengartner N.W., Klimenko A.V. et al., “Statistical reconstruction for cosmic ray muon tomography”, IEEE Trans. Image Process, 2007, 16(8):1985–1993.
  • Jin Jo W., Kim H., An J.S., Lee Y.C., Baek C., Chung Y.H., “Design of a muon tomography system with a plastic scintillator and wavelength-shifting fiber arrays”, Nuclear Instruments and Methods in Physics Research Section A, 2013, 732:568-572.
  • Roberts T.J., Beard K.B., Huang D., Ahmed S., Kaplan D.M., Spentzouris L.K., “G4Beamline Particle Tracking in Matter-dominated Beam Lines”, 11th European Conference, EPAC, 2008, Genoa. C 0806233, WEPP120.
  • Agostinelli S., Allison J., Amako K., Apostolakis J., Araujo H., Arce P. et al., “Geant4—a simulation toolkit”, Nuclear Instruments and Methods in Physics Research Section A, 2003, 506(3):250-303.
  • Allison J., Amako K., Apostolakis J., Araujo H., Arce P., Asai M. et al., “Geant4 developments and applications”, IEEE Transactions on Nuclear Science, 2006, 53(1):270-278.
  • Allison J., Amako K., Apostolakis J., Arce P., Asai M., Aso T. et al., “Recent developments in Geant4”, Nuclear Instruments and Methods in Physics Research Section A, 2016, 835:186-225.
  • Keras, (2021). https://keras.io. (Accesed 15 March 2021).
  • Tensorflow, (20121). https://www.tensorflow.org. (Accesed 15 March 2021).
  • Glorot X., Bordes A., Bengio Y., “Deep Sparse Rectifier Neural Networks”, Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, PMLR, 2011, Fort Lauderdale, 15:315-323.
  • Kingma D.P., Ba J., “Adam: A Method for Stochastic Optimization”, arXiv:1412.6980v9 [cs.LG], 2017.
  • Deng L., Li J., Huang J.T., Yao K., Yu D., Seide F. et al., “Recent advances in deep learning for speech research at Microsoft”, IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP, 2013, Vancouver: IEEE, 13859384.
  • Krizhevsky A., Sutskever I., Hinton G.E., “Imagenet classification with deep convolutional neural networks”, Communications of the ACM, 2017, 60(6):84-90. Hinton G.E., Salakhutdinov R.R., “Reducing the dimensionality of data with neural network”, Science, 2006, 313 (5786):504–507.
  • Hinton G., Deng L., Yu D., Dahl G.E., Mohamed A., Jaitly N. et al., “Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups”, Signal Processing Magazine, IEEE, 2012, 29(6):82–97.
  • Graves A., Mohamed A., Hinton G., “Speech recognition with deep recurrent neural networks”, International Conference on Acoustics, Speech and Signal Processing, ICASSP, 2013, Vancouver: IEEE, pp. 6645–6649.
  • Duchi J., Hazan E., Singer Y., “Adaptive subgradient methods for online learning and stochastic optimization”, The Journal of Machine Learning Research, 2011, 12:2121–2159.
  • Tieleman T., Hinton G., “Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude”, COURSERA: Neural Networks for Machine Learning, 2012, 4(2):26-31.

Çoklu Coulomb Saçılma Verileri ile Derin Sinir Ağlarını Kullanarak Müon Enerjisinin Tahmin Edilmesi

Yıl 2022, , 975 - 987, 30.09.2022
https://doi.org/10.31202/ecjse.1017848

Öz

Bu çalışma, yapay sinir ağlarında çoklu Coulomb saçılma verileri kullanılarak müon ışını enerjilerinin belirlenmesine dayanmaktadır. Müon parçacıkları, Geant4 tabanlı G4beamline benzetim programı kullanılarak 50 katmanlı bir kurşun nesneden saçıldı. Derin sinir ağları ile çalışmadan önce, katman sayısı cinsinden ortalama saçılma açısı dağılımları, müon ışını enerjilerini tahmin etmek için çoklu Coulomb saçılımı için iyi bilinen formül kullanılarak fit yöntemiyle analiz edildi. Daha sonra, müon ışını enerjisini tahmin etmek için derin sinir ağı yapılarında 1'den 10'a kadar katman sayısı üzerinden ortalama saçılma açıları kullanıldı. Derin sinir ağlarının, fit yöntemine göre çözünürlükleri önemli ölçüde iyileştirdiği gözlemlenmiştir.

Kaynakça

  • Tao W.M. et al. (Particle Data Group), “Review of Particle Physics”, J. Phys. G: Nucl. Part. Phys, 2006, 33(1):1-1232.
  • Moliere G.V., “Theorie der Streuung schneller geladener Teilchen I”, Z. Naturforschg A, 1947, 2a:133-145; Moliere G.V., “Theorie der Streuung schneller geladener Teilchen II”, Z. Naturforschg A. 1948, 3a:78-97.
  • Bethe H.A., “Molière's Theory of Multiple Scattering”, Phys. Rev. 1953, 89:1256.
  • Olbert S., “Application of the Multiple Scattering Theory to Cloud-Chamber Measurements I”, Phys. Rev, 1952, 87:319.
  • Annis M., Bridge H.S., Olbert S., “Application of the Multiple Scattering Theory to Cloud-Chamber Measurements II”, Phys. Rev., 1953, 89:1216.
  • Voyvodic L., Pickup E., “Multiple Scattering of Fast Particles in Photographic Emulsions”, Phys. Rev., 1952, 85:91.
  • Pinkau K., “Moliere's Theory of Multiple Scattering Applied to the Spark Chamber”, Z. Phys., 1966, 196(2):163-173.
  • Ambrosio M., Antolini R., Auriemma G., Bakari D., Baldini A., Barbarino G.C. et al., “Muon energy estimate through multiple scattering with the MACRO detector”, Nuclear Instruments and Methods in Physics Research Section A, 2002, 492(3): 376-386.
  • Ambrosio M., Antolini R., Bakari D., Baldini A., Barbarino G.C., Barish B.C. et al., “Atmospheric neutrino oscillations from upward throughgoing muon multiple scattering in MACRO”, Phys. Lett. B, 2003, 566(1-2): 35-44.
  • Ankowski A., Antonello M., Aprili P., Arneodo F., Badertscher A., Baiboussinov B. et al., “Measurement of through-going particle momentum by means of multiple scattering with the ICARUS T600 TPC”, Eur. Phys. J C, 2006, 48:667-676.
  • Borozdin K.N., Hogan G.E., Morris C., Priedhorsky C., Saunders A., Schultz L.J. et al., “Radiographic imaging with cosmic-ray muons”, Nature, 2003, 422: 277.
  • Pesente S., Vanini S., Benettoni M., Bonomi G., Calvini P., Checciha P. et al., “First results on material identification and imaging with a large-volume muon tomography prototype”, Nuclear Instruments and Methods in Physics Research Section A, 2009, 604(3):738-746.
  • Agafonova N., Aleksandrov A., Altinok O., Anokhina A., Aoki S., Ariga A. et al., “Momentum measurement by the multiple Coulomb scattering method in the OPERA lead-emulsion target”, New Journal of Physics, 2012, 14:013026 (19 pp).
  • Keskenler M., Dal, D., Aydin, T., “Yapay Zeka Destekli ÇOKS Yöntemi ile Kredi Kartı Sahtekarlığının Tespiti", El-Cezeri, 2021, 8(2):1007-1023.
  • Aylak B., Oral O., Yazıcı K., "Yapay Zeka ve Makine Öğrenmesi Tekniklerinin Lojistik Sektöründe Kullanımı", El-Cezeri, 2021, 8(1):74-93.
  • Çınar S., Bünyan Ünel F., "2/B orman vasfını yitirmiş araziden tarım arazisine dönüşen taşınmazların toplu değerlemesi", Geomatik, 2022, 7(2): 112-127.
  • Demiryege İ., Ulukavak M., "Derin öğrenme tabanlı iyonosferik TEC tahmini", Geomatik, 2022, 7(2): 80-87.
  • Öztürk A., Allahverdı N., Saday F., "Application of artificial intelligence methods for bovine gender prediction", Turkish Journal of Engineering, 2022, 6(1): 54-62.
  • Sertkaya C., Akçay S., "Giysi Endüstrisinde Üretim Performansının Tahmininde Yapay Sinir Ağlarının Kullanılması", Avrupa Bilim ve Teknoloji Dergisi, 2021, (28): 34-39.
  • Gemirter. C. B., Goularas D., "A Turkish Question Answering System Based on Deep Learning Neural Networks", Journal of Intelligent Systems: Theory and Applications, 2021 4(2): 65-75.
  • Darendeli B. N., Yılmaz A., "Convolutional Neural Network Approach to Predict Tumor Samples Using Gene Expression Data", Journal of Intelligent Systems: Theory and Applications, 2021, 4(2): 136-141.
  • Kamber E., Körpüz S., Can M., Yumurtacı Aydoğmuş H., Gümüş M., “Yapay Sinir Ağlarina Dayali Kısa Dönemli Elektrik Yükü Tahmini”, Endüstri Mühendisliği, 2021, 32 (2):364-379.
  • Grieder P.K.F., “Cosmic Rays at Earth”, 1st ed., Elsevier Science, Amsterdam (2001).
  • Hohlmann M., Ford P., Gnanvo K., Helsby J., Pena D., Hoch R., et al., “GEANT4 simulation of a cosmic ray muon tomography system with micropattern gas detectors for the detection of High Z materials”, IEEE Trans. Nulc Sci., 2009, 56(3):1356–1363.
  • Priedhorsky W.C., Borozdin K.N., Hogan G.E., Morris C., Saunders A., Schultz L.J. et al., “Detection of high-Z objects using multiple scattering of cosmic ray muons”, Review of Scientific Instruments, 2003, 74:4294.
  • Schultz L.J., Borozdin K.N., Gomez J.J., Hogan G.E., McGill J.A., Morris C.L. et al., “Image reconstruction and material discrimination via cosmic ray muon radiography”, Nuclear Instruments and Methods in Physics Research Section A, 2004, 519(3):687–694.
  • Schultz J.L., Blanpied G.S., Borozdin K.N., Fraser A.M., Hengartner N.W., Klimenko A.V. et al., “Statistical reconstruction for cosmic ray muon tomography”, IEEE Trans. Image Process, 2007, 16(8):1985–1993.
  • Jin Jo W., Kim H., An J.S., Lee Y.C., Baek C., Chung Y.H., “Design of a muon tomography system with a plastic scintillator and wavelength-shifting fiber arrays”, Nuclear Instruments and Methods in Physics Research Section A, 2013, 732:568-572.
  • Roberts T.J., Beard K.B., Huang D., Ahmed S., Kaplan D.M., Spentzouris L.K., “G4Beamline Particle Tracking in Matter-dominated Beam Lines”, 11th European Conference, EPAC, 2008, Genoa. C 0806233, WEPP120.
  • Agostinelli S., Allison J., Amako K., Apostolakis J., Araujo H., Arce P. et al., “Geant4—a simulation toolkit”, Nuclear Instruments and Methods in Physics Research Section A, 2003, 506(3):250-303.
  • Allison J., Amako K., Apostolakis J., Araujo H., Arce P., Asai M. et al., “Geant4 developments and applications”, IEEE Transactions on Nuclear Science, 2006, 53(1):270-278.
  • Allison J., Amako K., Apostolakis J., Arce P., Asai M., Aso T. et al., “Recent developments in Geant4”, Nuclear Instruments and Methods in Physics Research Section A, 2016, 835:186-225.
  • Keras, (2021). https://keras.io. (Accesed 15 March 2021).
  • Tensorflow, (20121). https://www.tensorflow.org. (Accesed 15 March 2021).
  • Glorot X., Bordes A., Bengio Y., “Deep Sparse Rectifier Neural Networks”, Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, PMLR, 2011, Fort Lauderdale, 15:315-323.
  • Kingma D.P., Ba J., “Adam: A Method for Stochastic Optimization”, arXiv:1412.6980v9 [cs.LG], 2017.
  • Deng L., Li J., Huang J.T., Yao K., Yu D., Seide F. et al., “Recent advances in deep learning for speech research at Microsoft”, IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP, 2013, Vancouver: IEEE, 13859384.
  • Krizhevsky A., Sutskever I., Hinton G.E., “Imagenet classification with deep convolutional neural networks”, Communications of the ACM, 2017, 60(6):84-90. Hinton G.E., Salakhutdinov R.R., “Reducing the dimensionality of data with neural network”, Science, 2006, 313 (5786):504–507.
  • Hinton G., Deng L., Yu D., Dahl G.E., Mohamed A., Jaitly N. et al., “Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups”, Signal Processing Magazine, IEEE, 2012, 29(6):82–97.
  • Graves A., Mohamed A., Hinton G., “Speech recognition with deep recurrent neural networks”, International Conference on Acoustics, Speech and Signal Processing, ICASSP, 2013, Vancouver: IEEE, pp. 6645–6649.
  • Duchi J., Hazan E., Singer Y., “Adaptive subgradient methods for online learning and stochastic optimization”, The Journal of Machine Learning Research, 2011, 12:2121–2159.
  • Tieleman T., Hinton G., “Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude”, COURSERA: Neural Networks for Machine Learning, 2012, 4(2):26-31.
Toplam 42 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Güral Aydın 0000-0002-4996-1174

Yayımlanma Tarihi 30 Eylül 2022
Gönderilme Tarihi 2 Kasım 2021
Kabul Tarihi 6 Haziran 2022
Yayımlandığı Sayı Yıl 2022

Kaynak Göster

IEEE G. Aydın, “Prediction of Muon Energy using Deep Neural Network with Multiple Coulomb Scattering Data”, ECJSE, c. 9, sy. 3, ss. 975–987, 2022, doi: 10.31202/ecjse.1017848.