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This paper presents a fast approach to simulating muons produced in interactions of the SPS proton beams with the target of the SHiP experiment.
1901
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1901
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1903
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A. Butter, T. Plehn, and R. Winterhalder, How to GAN LHC Events , arXiv:1907.03764
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T. Sjostrand, S. Mrenna, and P. Z. Skands, PYTHIA 6.4 Physics and Manual , JHEP (2006) , arXiv:hep-ph/0603175
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H. He, Y. Bai, E. A. Garcia, and S. Li, ADASYN: Adaptive synthetic sampling approach for imbalanced learning , 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)
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A. L. Maas, A. Y. Hannun, and A. Y. Ng, Rectifier Nonlinearities Improve Neural Network Acoustic Models , Proc. icml 30
2013
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2014
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2016
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2017
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2017
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2017
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B. Hosseini and W. M. Bonivento, "Particle Identification Tools and Performance in the SHiP Experiment" , CERN-SHiP-NOTE-2017-002 (2017)
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2014
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H. Dijkstra and T. Ruf, "Heavy Flavour Cascade Production in a Beam Dump" , CERN-SHiP-NOTE-2015-009 (2015)
2015
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E. Van Herwijnen, T. Ruf, M. Ferro-Luzzi, and H. Dijkstra, "Simulation and Pattern Recognition for the SHiP Spectrometer Tracker" , CERN-SHiP-NOTE-2015-002 (2015)
2015
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D. W. Scott, Multivariate density estimation: theory, practice, and visualization
2015
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2016
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2018
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2018
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2018
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HEP Software Foundation, J. Albrecht et al
2019
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D. Bick et al
2019
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