Optimising the Active Muon Shield for the SHiP Experiment at CERN

Baranov, A.; Burnaev, Evgeny; Ustyuzhanin, A.; Zaitsev, A.; Деркач, Д.; Klyuchnikov, Nikita; Ratnikov, F.; Lantwin, O.; Filatov, A.I.

doi:10.1088/1742-6596/934/1/012050

Cited by 9 publications

(11 citation statements)

References 19 publications

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“…For SHiP, detailed simulations have shown that the number of background events is expected to be very low, so that the experiment is "background free" [35,[53][54][55]. For MATHUSLA, the background is also expected to be low [18,19], although no simulation studies of background have been performed.…”

Section: Contentsmentioning

confidence: 99%

Sensitivity of the intensity frontier experiments for neutrino and scalar portals: analytic estimates

Bondarenko

Boyarsky

Ovchynnikov

et al. 2019

J. High Energ. Phys.

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In recent years, a number of intensity frontier experiments have been proposed to search for feebly interacting particles with masses in the GeV range. We discuss how the characteristic shape of the experimental sensitivity regions -upper and lower boundaries of the probed region, the maximal mass reach -depends on the parameters of the experiments. We use the SHiP and the MATHUSLA experiments as examples. We find a good agreement of our estimates with the results of the Monte Carlo simulations. This simple approach allows to cross-check and debug Monte Carlo results, to scan quickly over the parameter space of feebly interacting particle models, and to explore how sensitivity depends on the geometry of experiments.

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Section: Contentsmentioning

confidence: 99%

Sensitivity of the intensity frontier experiments for neutrino and scalar portals: analytic estimates

Bondarenko

Boyarsky

Ovchynnikov

et al. 2019

J. High Energ. Phys.

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“…3. 1 By the term epoch, we mean a single full pass through the training dataset. When making k discriminator updates per single generator update, this implies that, per epoch, the generator is trained on 1 k+1 th fraction of the training data, while the discriminator -on the remaining k k+1 -th fraction.…”

Section: Results and Validationmentioning

confidence: 99%

“…For the GAN objective, we use the Wasserstein distance [42] with the gradient penalty term from [43], as we find it resulting in the best generated data quality. We train both generator and discriminator using RMSprop optimizer with learning rates starting at 0.0001 at the beginning of the training process and decreasing by a factor of 0.999 after each epoch 1 . We make 8 discriminator update steps per single generator step.…”

Section: Network Architecture and The Objective Functionmentioning

confidence: 99%

“…Computer simulations of high-energy physics experiments play a crucial role in a variety of relevant tasks, including detector geometry optimization [1,2], selecting best analysis strategies [3,4], and testing the Standard Model (SM) predictions and searching for new phenomena beyond the SM [5,6]. For a typical experimental data analysis, the number of simulated events usually translates directly to the uncertainty a e-mail: artem.maevskiy@cern.ch (corresponding author) b e-mail: fedor.ratnikov@cern.ch c e-mail: alexander.zinchenko@jinr.ru d e-mail: riabovvg@gmail.com of the final physics result.…”

Section: Introductionmentioning

confidence: 99%

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Simulating the time projection chamber responses at the MPD detector using generative adversarial networks

et al. 2021

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High energy physics experiments rely heavily on the detailed detector simulation models in many tasks. Running these detailed models typically requires a notable amount of the computing time available to the experiments. In this work, we demonstrate a new approach to speed up the simulation of the Time Projection Chamber tracker of the MPD experiment at the NICA accelerator complex. Our method is based on a Generative Adversarial Network – a deep learning technique allowing for implicit estimation of the population distribution for a given set of objects. This approach lets us learn and then sample from the distribution of raw detector responses, conditioned on the parameters of the charged particle tracks. To evaluate the quality of the proposed model, we integrate a prototype into the MPD software stack and demonstrate that it produces high-quality events similar to the detailed simulator, with a speed-up of at least an order of magnitude. The prototype is trained on the responses from the inner part of the detector and, once expanded to the full detector, should be ready for use in physics tasks.

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“…Since the muon shield is a crucial part of the experiment, it has undergone several rounds of optimisation utilising machine learning methods [6,7]. The goal of the optimisation was to maximise physics performance, while minimising shield length and cost.…”

Section: The Ship Detectormentioning

confidence: 99%

Search for New Physics with the SHiP experiment at CERN

Shirobokov¹

2021

Proceedings of 40th International Conference on High Energy Physics — PoS(ICHEP2020)

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The SHiP Collaboration has proposed a general-purpose experimental facility operating in beam dump mode at the CERN SPS accelerator with the aim of searching for light, long-lived exotic particles. The detector system aims at measuring the visible decays of hidden sector particles to both fully reconstructible final states and to partially reconstructible final states with neutrinos, in a nearly background free environment. In addition to that, it can detect light dark matter via its scattering and study tau neutrino physics. Using a high-intensity beam of 400 GeV protons, the experiment is capable of integrating 2 × 10 20 protons in five years, which allows probing dark photons, dark scalars, axion-like particles and heavy neutral leptons with GeV-scale masses at sensitivities that exceed by orders of magnitude those of existing and projected experiments. The sensitivity to heavy neutrinos will allow for the first time to probe, in the mass range between the kaon and the charm meson mass, a coupling range for which baryogenesis and the magnitude of the active neutrino masses can be explained. The sensitivity to light dark matter reaches well below the elastic scalar dark matter relic density limits in the range from a few MeV/c 2 up to 200 MeV/c 2 .

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Optimising the Active Muon Shield for the SHiP Experiment at CERN

Cited by 9 publications

References 19 publications

Sensitivity of the intensity frontier experiments for neutrino and scalar portals: analytic estimates

Sensitivity of the intensity frontier experiments for neutrino and scalar portals: analytic estimates

Simulating the time projection chamber responses at the MPD detector using generative adversarial networks

Search for New Physics with the SHiP experiment at CERN

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