Fast simulation of muons produced at the SHiP experiment using Generative Adversarial Networks

2019-11-01
Ahdida, C.
Albanese, R.
Alexandrov, A.
Anokhina, A.
Aoki, S.
Arduini, G.
Atkin, E.
Azorskiy, N.
Back, J. J.
Bagulya, A.
Dos Santos, F. Baaltasar
Baranov, A.
Bardou, F.
Barker, G. J.
Battistin, M.
Bauche, J.
Bay, A.
Bayliss, V
Bencivenni, G.
Berdnikov, A. Y.
Berdnikov, Y. A.
Berezkina, I
Bertani, M.
Betancourt, C.
Bezshyiko, I
Bezshyyko, O.
Bick, D.
Bieschke, S.
Blanco, A.
Boehm, J.
Bogomilov, M.
Bondarenko, K.
Bonivento, W. M.
Borburgh, J.
Boyarsky, A.
Brenner, R.
Breton, D.
Brundler, R.
Bruschi, M.
Bscher, V
Buonaura, A.
Buontempo, S.
Cadeddu, S.
Calcaterra, A.
Calviani, M.
Campanelli, M.
Casolino, M.
Charitonidis, N.
Chau, P.
Chauveau, J.
Chepurnov, A.
Chernyavskiy, M.
Choi, K-Y
Chumakov, A.
Ciambrone, P.
Congedo, L.
Cornelis, K.
Cristinziani, M.
Crupano, A.
Dallavalle, G. M.
Datwyler, A.
D'Ambrosio, N.
D'Appollonio, G.
De Carvalho Saraiva, J.
De Lellis, G.
de Magistris, M.
De Roeck, A.
De Serio, M.
De Simone, D.
Dedenko, L.
Dergachev, P.
Di Crescenzo, A.
Di Marco, N.
Dib, C.
Dijkstra, H.
Dipinto, P.
Dmitrenko, V
Dmitrievskiy, S.
Dougherty, L. A.
Dolmatov, A.
Domenici, D.
Donskov, S.
Drohan, V
Dubreuil, A.
Ehlert, M.
Enik, T.
Etenko, A.
Fabbri, F.
Fabbri, L.
Fabich, A.
Fedin, O.
Fedotovs, F.
Felici, G.
Ferro-Luzzi, M.
Filippov, K.
Fini, R. A.
Fonte, P.
Franco, C.
Fraser, M.
Fresa, R.
Froeschl, R.
Fukuda, T.
Galati, G.
Gall, J.
Gatignon, L.
Gavrilov, G.
Gentile, V
Gerlach, S.
Goddard, B.
Golinka-Bezshyyko, L.
Golovatiuk, A.
Golubkov, D.
Golutvin, A.
Gorbounov, P.
Gorbunov, D.
Gorbunov, S.
Gorkavenko, V
Gornushkin, Y.
Gorshenkov, M.
Grachev, V
Grandchamp, A. L.
Granich, G.
Graverini, E.
Grenard, J-L
Grenier, D.
Grichine, V
Gruzinskii, N.
Güler, Ali Murat
Guz, Yu
Haefeli, G. J.
Hagner, C.
Hakobyan, H.
Harris, I. W.
van Herwijnen, E.
Hessler, C.
Hollnagel, A.
Hosseini, B.
Hushchyn, M.
Iaselli, G.
Iuliano, A.
Ivantchenko, V
Jacobsson, R.
Jokovic, D.
Jonker, M.
Kadenko, I
Kain, V
Kaiser, B.
Kamiscioglu, C.
Kershaw, K.
Khabibullin, M.
Khalikov, E.
Khaustov, G.
Khoriauli, G.
Khotyantsev, A.
Kim, S. H.
Kim, Y. G.
Kim, V
Kitagawa, N.
Ko, J-W
Kodama, K.
Kolesnikov, A.
Kolev, D.
Kolosov, V
Komatsu, M.
Kondrateva, N.
Kono, A.
Konovalova, N.
Kormannshaus, S.
Korol, I
Korol'ko, I
Korzenev, A.
Kostyukhin, V
Platia, E. Koukovini
Kovalenko, S.
Krasilnikova, I
Kudenko, Y.
Kurbatov, E.
Kurbatov, P.
Kurochka, V
Kuznetsova, E.
Lacker, H. M.
Lamont, M.
Lanfranchi, G.
Lantwin, O.
Lauria, A.
Lee, K. S.
Lee, K. Y.
Levy, J-M
Loschiavo, V. P.
Lopes, L.
Sola, E. Lopez
Lyubovitskij, V
Maalmi, J.
Magnan, A.
Maleev, V
Malinin, A.
Manabe, Y.
Managadze, A. K.
Manfredi, M.
Marsh, S.
Marshall, A. M.
Mefodev, A.
Mermod, P.
Miano, A.
Mikado, S.
Mikhaylov, Yu
Milstead, D. A.
Mineev, O.
Montanari, A.
Montesi, M. C.
Morishima, K.
Movchan, S.
Muttoni, Y.
Naganawa, N.
Nakamura, M.
Nakano, T.
Nasybulin, S.
Ninin, P.
Nishio, A.
Novikov, A.
Obinyakov, B.
Ogawa, S.
Okateva, N.
Opitz, B.
Osborne, J.
Ovchynnikov, M.
Owtscharenko, N.
Owen, P. H.
Pacholek, P.
Paoloni, A.
Park, B. D.
Park, S. K.
Pastore, A.
Patel, M.
Pereyma, D.
Perillo-Marcone, A.
Petkov, G. L.
Petridis, K.
Petrov, A.
Podgrudkov, D.
Poliakov, V
Polukhina, N.
Prieto, J. Prieto
Prokudin, M.
Prota, A.
Quercia, A.
Rademakers, A.
Rakai, A.
Ratnikov, F.
Rawlings, T.
Redi, F.
Ricciardi, S.
Rinaldesi, M.
Rodin, Volodymyr
Rodin, Viktor
Robbe, P.
Cavalcante, A. B. Rodrigues
Roganova, T.
Rokujo, H.
Rosa, G.
Rovelli, T.
Ruchayskiy, O.
Ruf, T.
Samoylenko, V
Samsonov, V
Galan, F. Sanchez
Diaz, P. Santos
Ull, A. Sanz
Saputi, A.
Sato, O.
Savchenko, E. S.
Schliwinski, J. S.
Schmidt-Parzefall, W.
Serra, N.
Sgobba, S.
Shadura, O.
Shakin, A.
Shaposhnikov, M.
Shatalov, P.
Shchedrina, T.
Shchutska, L.
Shevchenko, V
Shibuya, H.
Shihora, L.
Shirobokov, S.
Shustov, A.
Silverstein, S. B.
Simone, S.
Simoniello, R.
Skorokhvatov, M.
Smirnov, S.
Sohn, J. Y.
Sokolenko, A.
Solodko, E.
Starkov, N.
Stoel, L.
Storaci, B.
Stramaglia, M. E.
Sukhonos, D.
Suzuki, Y.
Takahashi, S.
Tastet, J. L.
Teterin, P.
Naing, S. Than
Timiryasov, I
Tioukov, V
Tommasini, D.
Torii, M.
Tosi, N.
Treille, D.
Tsenov, R.
Ulin, S.
Ustyuzhanin, A.
Uteshev, Z.
Vankova-Kirilova, G.
Vannucci, F.
Venkova, P.
Venturi, V.
Vilchinski, S.
Villa, M.
Vincke, Heinz
Vincke, Helmut
Visone, C.
Vlasik, K.
Volkov, A.
Voronkov, R.
van Waasen, S.
Wanke, R.
Wertelaers, P.
Woo, J-K
Wurm, M.
Xella, S.
Yilmaz, D.
Yilmazer, A. U.
Yoon, C. S.
Zarubin, P.
Zarubina, I
Zaytsev, Yu
This paper presents a fast approach to simulating muons produced in interactions of the SPS proton beams with the target of the SHiP experiment. The SHIP experiment will be able to search for new long-lived particles produced in a 400 GeV/c SPS proton beam dump and which travel distances between fifty metres and tens of kilometers. The SHiP detector needs to operate under ultra-low background conditions and requires large simulated samples of muon induced background processes. Through the use of Generative Adversarial Networks it is possible to emulate the simulation of the interaction of 400 GeV/c proton beams with the SHiP target, an otherwise computationally intensive process. For the simulation requirements of the SHiP experiment, generative networks are capable of approximating the full simulation of the dense fixed target, offering a speed increase by a factor of O(10(6)). To evaluate the performance of such an approach, comparisons of the distributions of reconstructed muon momenta in SHiP's spectrometer between samples using the full simulation and samples produced through generative models are presented. The methods discussed in this paper can be generalised and applied to modelling any non-discrete multi-dimensional distribution.
JOURNAL OF INSTRUMENTATION

Suggestions

Modelling and Monte Carlo simulation of the atomic ordering processes in Ni3Al intermetallics
Mehrabov, Amdulla; Akdeniz, Mahmut Vedat (IOP Publishing, 2007-03-01)
The evolution of atomic ordering processes in Ni3Al has been modelled by a Monte Carlo ( MC) simulation method combined with the electronic theory of alloys in pseudopotential approximation. The magnitudes of atomic ordering energies of atomic pairs in the Ni3Al system have been calculated by means of electronic theory in pseudopotential approximation up to the 4th coordination spheres and subsequently used as input data for MC simulation for more detailed analysis for the first time. The Bragg - Williams l...
Coupled thermoviscoplasticity of glassy polymers in the logarithmic strain space based on the free volume theory
Miehe, Christian; Mendez Diez, Joel; Göktepe, Serdar; Schaenzel, Lisa Marie (Elsevier BV, 2011-06-15)
The paper outlines a constitutive model for finite thermo-visco-plastic behavior of amorphous glassy polymers and considers details of its numerical implementation. In contrast to existing kinematical approaches to finite plasticity of glassy polymers, the formulation applies a plastic metric theory based on an additive split of Lagrangian Hencky-type strains into elastic and plastic parts. The analogy between the proposed formulation in the logarithmic strain space and the geometrically linear theory of pl...
Two-Dimensional Numerical Analysis of Phosphorus Diffused Emitters on Black Silicon Surfaces
TÜRKAY, Deniz; Yerci, Selçuk (2018-07-06)
In this work, we present an analysis on electrical performance of phosphorus diffused emitters on black silicon surfaces through two-dimensional simulations. In particular, we focus on the extraction and analysis of the emitter saturation current density (J(0e)), the sheet resistance (R-sh), spatial collection efficiency profile and relatedly J(sc) of a solar cell. Using process simulations, we form emitters on periodic triangular structures with various aspect ratios (R) and emitter profiles. We show that ...
Multi-scale characterization of particle clustering in discontinuously reinforced composites
CETIN, Arda; Kalkanlı, Ali (Elsevier BV, 2009-06-01)
The applicability of a quantitative characterization scheme for cluster detection in particle reinforced composites is discussed. The method considers the pattern from the perspective of individual particles, so that even in a pattern that globally conforms to a random distribution, micro-scale heterogeneities can be detected. The detected clusters are visualized by kernel surfaces. Results indicate that the presented methodology is an effective discriminator of clusters and can successfully be used for qua...
Simulation of dissolution of silicon in an indium solution by spectral methods
Coskun, AU; Yener, Y; Arinc, F (IOP Publishing, 2002-09-01)
The results of a numerical simulation of natural convection due to concentration gradients during dissolution of silicon in an indium solution in a horizontal substrate-solution-substrate system are presented. The Chebyshev-Tau spectral method has been used for the simulations. The results are in very good agreement with the experimental data available in the literature. It is concluded that the discrepancies in the dissolution depths between the previous simulations and experimental data, especially at the...
Citation Formats
C. Ahdida et al., “Fast simulation of muons produced at the SHiP experiment using Generative Adversarial Networks,” JOURNAL OF INSTRUMENTATION, vol. 14, pp. 0–0, 2019, Accessed: 00, 2023. [Online]. Available: https://hdl.handle.net/11511/102360.