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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. 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 (106). 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.
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 (106). 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.
Fast simulation of muons produced at the SHiP experiment using Generative Adversarial Networks
Ahdida C.;Albanese R. M.;Alexandrov A.;Anokhina A.;Aoki S.;Arduini G.;Atkin E.;Azorskiy N.;Back J. J.;Bagulya A.;Santos F. B. D.;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.;Buscher 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.;Saraiva J. D. C.;Lellis G. D.;De Magistris M.;Roeck A. D.;De Serio M.;Simone D. D.;Dedenko L.;Dergachev P.;Crescenzo A. D.;Marco N. D.;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.;Guler A. M.;Guz Y.;Haefeli G. J.;Hagner C.;Hakobyan H.;Harris I. W.;Herwijnen E. V.;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. I.;Kolosov V.;Komatsu M.;Kondrateva N.;Kono A.;Konovalova N.;Kormannshaus S.;Korol I.;Korol'ko I.;Korzenev A.;Kostyukhin V.;Platia E. K.;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. L.;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 Y.;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. P.;Prokudin M.;Prota A.;Quercia A.;Rademakers A.;Rakai A.;Ratnikov F.;Rawlings T.;Redi F.;Ricciardi S.;Rinaldesi M.;Rodin V.;Rodin V.;Robbe P.;Cavalcante A. B. R.;Roganova T.;Rokujo H.;Rosa G.;Rovelli T.;Ruchayskiy O.;Ruf T.;Samoylenko V.;Samsonov V.;Galan F. S.;Diaz P. S.;Ull A. S.;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. T.;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 H.;Vincke H.;Visone C.;Vlasik K.;Volkov A.;Voronkov R.;Waasen S. V.;Wanke R.;Wertelaers P.;Woo J. -K.;Wurm M.;Xella S.;Yilmaz D.;Yilmazer A. U.;Yoon C. S.;Zarubin P.;Zarubina I.;Zaytsev Y.
2019-01-01
Abstract
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 (106). 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.
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 (106). 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.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11586/373136
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simulazione ASN
Il report seguente simula gli indicatori relativi alla propria produzione scientifica in relazione alle soglie ASN 2023-2025 del proprio SC/SSD. Si ricorda che il superamento dei valori soglia (almeno 2 su 3) è requisito necessario ma non sufficiente al conseguimento dell'abilitazione. La simulazione si basa sui dati IRIS e sugli indicatori bibliometrici alla data indicata e non tiene conto di eventuali periodi di congedo obbligatorio, che in sede di domanda ASN danno diritto a incrementi percentuali dei valori. La simulazione può differire dall'esito di un’eventuale domanda ASN sia per errori di catalogazione e/o dati mancanti in IRIS, sia per la variabilità dei dati bibliometrici nel tempo. Si consideri che Anvur calcola i valori degli indicatori all'ultima data utile per la presentazione delle domande.
La presente simulazione è stata realizzata sulla base delle specifiche raccolte sul tavolo ER del Focus Group IRIS coordinato dall’Università di Modena e Reggio Emilia e delle regole riportate nel DM 589/2018 e allegata Tabella A. Cineca, l’Università di Modena e Reggio Emilia e il Focus Group IRIS non si assumono alcuna responsabilità in merito all’uso che il diretto interessato o terzi faranno della simulazione. Si specifica inoltre che la simulazione contiene calcoli effettuati con dati e algoritmi di pubblico dominio e deve quindi essere considerata come un mero ausilio al calcolo svolgibile manualmente o con strumenti equivalenti.
