Sensitivity of the SHiP experiment to Heavy Neutral Leptons

Ahdida, C.; Albanese, R.; Alexandrov, A.; Anokhina, A.; Aoki, Shigeki; Arduini, G.; Atkin, E.; Azorskiy, N.; Santos, F. Baaltasar dos; Back, J. J.; Bagulya, A.; Baranov, A.; Bardou, F.; Barker, G. J.; Battistin, M.; Bauche, J.; Bay, A.; Bayliss, V.; Bencivenni, G.; Berdnikov, Y.; Berdnikov, A.; Berezkina, I.; Bertani, M.; Betancourt, C.; Bezshyiko, Ia.; Bezshyyko, Oleg; Bick, D.; Bieschke, S.; Blanco, A.; Boehm, J.; Bogomilov, M.; Bondarenko, Kyrylo; Bonivento, W.; Borburgh, J.; Boyarsky, Alexey; Brenner, R.; Bretón, D.; Brundler, R.; Bruschi, M.; Büscher, V.; Buonaura, A.; Buontempo, S.; Cadeddu, S.; Calcaterra, A.; Calviani, M.; Campanelli, M.; Casolino, M.; Charitonidis, N.; Chau, Phi; Chauveau, J.; Chepurnov, A.; Chernyavskiy, M.; Choi, Ki Young; Chumakov, A.G.; Ciambrone, P.; Cornelis, Karel; Cristinziani, M.; Crupano, A.; Dallavalle, G. M.; Datwyler, A.; D’Ambrosio, N.; D’Appollonio, Giuseppe; Dedenko, L. G.; Dergachev, Pavel; Saraiva, J. de Carvalho; Lellis, G. De; Magistris, M. de; Roeck, A. De; Serio, M. De; Simone, D. De; Dib, Claudio O.; Dijkstra, H.; Dipinto, P.; Crescenzo, A. Di; Marco, N. Di; Дмитренко, В. В.; Dmitrievskiy, S.; Dolmatov, A.; Domenici, D.; Donskov, S.V.; Dougherty, L.A.; Drohan, V.; Dubreuil, A.; Ebert, J.; Enik, T.; Etenko, A.; Fabbri, F.; Fabbri, L.; Fabich, A.; Fedin, O. L.; Fedotovs, F.; Ferro‐Luzzi, M.; Felici, G.; Filippov, K.; Fini, R. A.; Fonte, P.; Franco, C.; Fraser, Matthew; Fresa, R.; Froeschl, R.; Fukuda, Tatsuya; Galati, G.; Gall, J.; Gatignon, L.; Gavrilov, G.; Gentile, V.; Goddard, B.; Golinka-Bezshyyko, L.; Golovatiuk, A.; Golubkov, D.; Golutvin, A.; Gorbounov, P. A.; Горбунов, С.; Gorbunov, D.; Gorkavenko, Volodymyr M.; Gornushkin, Y.; Горшенков, М.В.; Grachev, Vadim; Grandchamp, A.L.; Granich, G.; Graverini, E.; Grenard, Jean-Louis; Grenier, D.; Grichine, V.; Gruzinskii, N.; Guz, Yu.; Haefeli, G.; Hagner, C.; Hakobyan, H.; Harris, Irene; Heßler, Christoph; Hollnagel, A.; Hosseini, B.; Hushchyn, M.; Iaselli, G.; Iuliano, A.; Ivantchenko, Vladimir; Jacobsson, R.; Joković, D.; Jonker, M.; Каденко, І.; Kain, V.; Kamiscioglu, C.; Kershaw, K.; Khabibullin, M.; Khalikov, E.; Khaustov, G.; Khoriauli, G.; Khotyantsev, A.; Kim, Y. G.; Kim, V.; Kim, S. H.; Kitagawa, N.; Ko, J.-W.; Kodama, K.; Kolesnikov, A.; Kolev, D.; Kolosov, V.N.; Komatsu, M.; Kondrat’eva, N. V.; Kono, Akihiro; Konovalova, N. S.; Kormannshaus, S.; Korol, I.; Korolko, I.; Korzenev, A.; Kostyukhin, V. V.; Platia, E. Koukovini; Kovalenko, Sergey; Krasilnikova, I.; Kudenko, Y.; Kurbatov, E.; Kurbatov, Pavel; Kurochka, V.; Kuznetsova, E.; Lacker, H. M.; Lamont, Mike; Lanfranchi, G.; Lantwin, O.; Lauria, A.; Lee, K. S.; Lee, K. Y.; Lévy, J. M.; Lopes, L.; Sola, E. Lopez; Loschiavo, V. P.; Lyubovitskij, Valery E.; Guler, A. M.; Maalmi, Jihane; Magnan, A.-M.; Maleev, V. P.; Малинин, А.; Manabe, Yuta; Managadze, A. K.; Manfredi, M.; Marsh, S.; Marshall, Alexander Mclean; Mefodev, A.; Mermod, P.; Miano, Andrea; Mikado, S.; Mikhaylov, Yu.; Milstead, D. A.; Mineev, O.; Montanari, A.; Montesi, M.C.; Morishima, Keisuke; Movchan, S.; Muttoni, Y.; Naganawa, N.; Nakamura, Masahiro; Nakano, Takashi; Nasybulin, S.; Ninin, P.; Nishio, Akira; Novikov, Alexander; Obinyakov, B.; Ogawa, S.; Okateva, N.; Opitz, B.; Osborne, John A.; Ovchynnikov, Maksym; Owen, P.; Owtscharenko, N.; Pacholek, P.; Paoloni, A.; Paparella, Rocco; D., Park, B.; Park, S. K.; Pastore, A.; Patel, M.; Pereyma, D.; Perillo‐Marcone, Antonio; Petkov, G.L.; Petridis, K.; Petrov, A.; Подгрудков, Д.; Poliakov, V.; Polukhina, N.; Prieto, Javier; Prokudin, M.; Prota, Andrea; Quercia, A.; Rademakers, A.; Rakai, A.; Ratnikov, F.; Rawlings, T.; Redi, F.; Ricciardi, S.; Rinaldesi, M.; Robbe, P.; Rodin, Viktor; Rodin, V.; Cavalcante, A.B. Rodrigues; Роганова, Т. М.; Rokujo, H.; Rosa, G.; Rovelli, T.; Ruchayskiy, Oleg; Ruf, T.; Samoylenko, V.D.; Samsonov, V.; Galan, F. Sanchez; Díaz, P. Santos; Ull, A. Sanz; Saputi, A.; Sato, O.; Savchenko, E. S.; Schmidt‐Parzefall, W.; Serra, N.; Sgobba, S.; Shadura, O.; Шакин, А.; Shaposhnikov, Mikhail; Шаталов, П. Б.; Shchedrina, T. V.; Shchutska, L.; Shevchenko, V.; Shibuya, H.; Shirobokov, S.; Shustov, A.; Silverstein, S. B.; Simone, S.; Simoniello, R.; Skorokhvatov, M.; Smirnov, S. Yu.; Sohn, J. Y.; Sokolenko, Anastasia; Solodko, E.; Старков, Н. И.; Stoel, Linda; Storaci, B.; Stramaglia, M. E.; Sukhonos, D.; Suzuki, Y.; Takahashi, Satoshi; Tastet, Jean‐Loup; Teterin, P.; Naing, S. Than; Timiryasov, Inar; Tioukov, V.; Tommasini, D.; Torii, M.; Tosi, N.; Treille, D.; Tsenov, R.; Улин, С. Е.; Ustyuzhanin, A.; Uteshev, Z. M.; Vankova-Kirilova, G.; Vannucci, F.; Herwijnen, E. van; Waasen, Stefan van; Venkova, P.; Venturi, V.; Vilchinski, S.; Villa, M.; Vincke, Heinz; Vincke, Helmut; Visone, C.; Власик, К. Ф.; Volkov, A.; Voronkov, R.; Wanke, R.; Wertelaers, P.; Woo, Jong-Kwan; Wurm, M.; Xella, S.; Yılmaz, Deniz; Yılmazer, A.U.; Yoon, C. S.; Zarubin, P. I.; Zarubina, I. G.; Zaytsev, Yu.

doi:10.1007/jhep04(2019)077

Cited by 109 publications

(122 citation statements)

References 77 publications

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“…Since the network acts as a variable transformation, its gradient must be computed for each inference point in order to determine the Jacobian. This becomes computationally heavy for high multiplicity processes.A completely orthogonal approach utilizes machine learning techniques directly for amplitude evaluation [17] or event generation [18][19][20][21][22][23][24]. Training data for these approaches are obtained from traditional event generation techniques, and hence the problem of efficient event generation still remains.…”

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confidence: 99%

Event generation with normalizing flows

et al. 2020

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We present a novel integrator based on normalizing flows which can be used to improve the unweighting efficiency of Monte-Carlo event generators for collider physics simulations. In contrast to machine learning approaches based on surrogate models, our method generates the correct result even if the underlying neural networks are not optimally trained. We exemplify the new strategy using the example of Drell-Yan type processes at the LHC, both at leading and partially at nextto-leading order QCD. I. INTRODUCTIONNumerical simulation programs are a cornerstone of collider physics. They are used for the planning of future experiments, analysis of current measurements and, finally, reinterpretation based on an improved theoretical understanding of nature. They employ Monte Carlo methods to link theory and experiment by generating virtual collider events, which can then be analyzed like actual events observed in detectors [1,2].With more and more data available from the Large Hadron Collider (LHC) and the high-luminosity upgrade, the task of simulating collisions at high precision becomes a matter of concern for the high-energy physics community. The projected amount of computational resources falls far short of the needs for precision event generation [3]. Past studies of the scaling behavior of multi-jet simulations have shown that the compute needs are largely determined by the gradually decreasing unweighting efficiency [4,5]. Except for dedicated integrators, which require a detailed understanding of the physics problem at hand, adaptive Monte-Carlo methods seem the only choice to address this problem [6][7][8][9][10][11][12][13].With the rise of machine learning, this topic has seen a resurgence of interest recently. The possibility of using these techniques for integration in high-energy physics was first discussed in Ref. [14]. Boosted Decision Trees and Generative Adversarial Networks (GANs) were investigated as possible general purpose integrators. This new technique improved the integration of non-separable high dimensional functions, for which traditional algorithms failed. The first true physics application was presented in Ref. [15]. The authors used Dense Neural Networks (DNN) in order to perform a variable transformation and demonstrate that they obtain significantly larger efficiencies for three body decay integrals than standard approaches [16]. The major drawback of this method is its computational cost. Since the network acts as a variable transformation, its gradient must be computed for each inference point in order to determine the Jacobian. This becomes computationally heavy for high multiplicity processes.A completely orthogonal approach utilizes machine learning techniques directly for amplitude evaluation [17] or event generation [18][19][20][21][22][23][24]. Training data for these approaches are obtained from traditional event generation techniques, and hence the problem of efficient event generation still remains. In addition, one needs to ensure that the neural networks are trained well ...

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confidence: 99%

Event generation with normalizing flows

et al. 2020

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“…Utilising a 400 GeV proton beam from the CERN Super Proton Synchrotron, it is expected to be sensitive to sterile neutrinos with m N up to the B c meson mass (∼ 6 GeV). In a benchmark scenario where the electron-sterile coupling dominates, SHiP is expected to be sensitive down to |V eN | 2 10 −10 for m N ≈ 1.6 GeV [101].…”

Section: Meson Decays and Beam-dump Experimentsmentioning

confidence: 99%

Neutrinoless double beta decay versus other probes of heavy sterile neutrinos

Bolton

Deppisch

Dev³

2020

J. High Energ. Phys.

171

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We make a comparative study of the neutrinoless double beta decay constraints on heavy sterile neutrinos versus other direct and indirect constraints from both lepton number conserving and violating processes, as a sensitive probe of the extent of lepton number violation and possible interference effects in the sterile sector. To this effect, we introduce a phenomenological parametrisation of the simplified one-generation seesaw model with one active and two sterile neutrino states in terms of experimentally measurable quantities, such as active-sterile neutrino mixing angles, CP phases, masses and mass splittings. This simple parametrisation enables us to analytically derive a spectrum of possible scenarios between the canonical seesaw with purely Majorana heavy neutrinos and inverse seesaw with pseudo-Dirac ones. We then go on to constrain the simplified parameters of this model from various experiments in energy, intensity and cosmic frontiers. We emphasise that the constraints from lepton number violating processes strongly depend on the mass splitting between the two sterile states and the relative CP phase between them. This is particularly relevant for the neutrinoless double beta decay constraint, which could now get significantly weaker for small mass splitting and opposite CP parities between the sterile states. It is important to keep this in mind while comparing the neutrinoless double beta decay constraint with the direct search limits in the electron flavour from high-energy colliders.

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“…The SHiP experiment is only sensitive to GeV-scale HNLs, with mixing angles significantly above the seesaw limit [20]. Therefore it can only probe the quasi-Dirac regime described above.…”

Section: Coherent Oscillations Of Heavy Neutral Leptonsmentioning

confidence: 99%

“…From the FIP (feebly interacting particles) search point of view [11], HNLs with masses below that of a B meson are the most accessible in the foreseeable future. There is a vast program to search for HNLs at intensity frontier experiments, either LHC-based, such as MATHUSLA [12,13], FASER [14], CODEX-b [15] and AL3X [16], or at beam-dump facilities, such as NA62 ++ [17] and SHiP [18][19][20]. The latter two experiments will be sensitive to both the mass and mixing angles of HNLs.…”

Section: Introductionmentioning

confidence: 99%

Dirac vs. Majorana HNLs (and their oscillations) at SHiP

Tastet

Timiryasov

2020

J. High Energ. Phys.

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SHiP is a proposed high-intensity beam dump experiment set to operate at the CERN SPS. It is expected to have an unprecedented sensitivity to a variety of models containing feebly interacting particles, such as Heavy Neutral Leptons (HNLs). Two HNLs or more could successfully explain the observed neutrino masses through the seesaw mechanism. If, in addition, they are quasi-degenerate, they could be responsible for the baryon asymmetry of the Universe. Depending on their mass splitting, HNLs can have very different phenomenologies: they can behave as Majorana fermions-with lepton number violating (LNV) signatures, such as same-sign dilepton decays-or as Dirac fermions with only lepton number conserving (LNC) signatures. In this work, we quantitatively demonstrate that LNV processes can be distinguished from LNC ones at SHiP, using only the angular distribution of the HNL decay products. Accounting for spin correlations in the simulation and using boosted decision trees for discrimination, we show that SHiP will be able to distinguish Majorana-like and Dirac-like HNLs in a significant fraction of the currently unconstrained parameter space. If the mass splitting is of order 10 −6 eV, SHiP could even be capable of resolving HNL oscillations, thus providing a direct measurement of the mass splitting. This analysis highlights the potential of SHiP to not only search for feebly interacting particles, but also perform model selection.

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Sensitivity of the SHiP experiment to Heavy Neutral Leptons

Cited by 109 publications

References 77 publications

Event generation with normalizing flows

Event generation with normalizing flows

Neutrinoless double beta decay versus other probes of heavy sterile neutrinos

Dirac vs. Majorana HNLs (and their oscillations) at SHiP

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