Development of shashlik electromagnetic calorimeter for the NICA/MPD

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Characterizing uncertainty is a common issue in nuclear measurement and has important implications for reliable physical discovery. Traditional methods are either insufficient to cope with the heterogeneous nature of uncertainty or inadequate to perform well with unknown mathematical models. In this paper, we propose using multi-layer convolutional neural networks for empirical uncertainty estimation and feature extraction of nuclear pulse signals. This method is based on deep learning, a recent development of machine learning techniques, which learns the desired mapping function from training data and generalizes to unseen test data. Furthermore, ensemble learning is utilized to estimate the uncertainty originating from trainable parameters of the network and to improve the robustness of the whole model. To evaluate the performance of the proposed method, simulation studies, in comparison with curve fitting, investigate extensive conditions and show its universal applicability. Finally, a case study is made using data from a NICA-MPD electromagnetic calorimeter module exposed to a test beam at DESY, Germany. The uncertainty estimation method successfully detected out-of-distribution samples and also achieved good accuracy in time and energy measurements.

Section: Jinst 17 P02032mentioning

confidence: 99%

Universal uncertainty estimation for nuclear detector signals with neural networks and ensemble learning

Ai¹,

Deng²,

Wang³

et al. 2022

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“…The lead plates of prototypes produced in 2018 are used as the reference standard because the prototypes had already met the performance requirements [5]. The reflectivity of the lead plate of the prototype measured by the same method is 80%.…”

Section: Jinst 17 T04005mentioning

confidence: 99%

“…Previous shashlik electromagnetic calorimeters have demonstrated excellent energy resolution (better than √ 5%/𝐸 GeV) [1][2][3][4]. This technology has been adopted to construct the full barrel electromagnetic calorimeter detector for the MPD experiment at NICA [5]. The cylindrical ECal system consists of 50 half-sectors, each half-sector consists of 48 ECal modules, and each module consists of 16 towers, which means a total of 38,400 towers will be produced.…”

Section: Introductionmentioning

confidence: 99%

Production and quality control of NICA-MPD shashlik electromagnetic calorimeter in Tsinghua University

Li¹,

Shen²,

Zhang³

et al. 2022

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The Multi Purpose Detector (MPD) is one of the detectors at the NICA collider and the Electromagnetic Calorimeter (ECal) is an important part of MPD. The cylindrical ECal system consists of 2400 ECal modules. In the first stage (2020–2024), Tsinghua University (THU) will produce 460 ECal modules for MPD project. This article mainly introduces the production, quality control and cosmic ray test of ECal modules in THU. The process flow, quality control standards, and testing system of ECal production have been established, which guarantees the high-quality completion of mass production on schedule. Up to now, nearly 360 modules have been manufactured, and a total of 460 modules will be produced by June 2022.

“…In order to enable NICA to probe nuclear structures in low-energy regions QCD, ECal is required to have an energy resolution of better than 5%/ √ 𝐸 (GeV) for high-energy electrons with 2-3.5 GeV. A novel calorimeter -Shashlik ECal has the advantages of high energy resolution, high position resolution, high time resolution, and high particle recognition [13], thus it is used in MPD.…”

Section: Introductionmentioning

confidence: 99%

Simulation study on photon generation and collection of tower applied to NICA-MPD electromagnetic calorimeter

Peng¹,

Ke-di²,

Ji³

et al. 2022

Shashlik tower, which is composed of absorbers and scintillators alternately, surrounded by reflector and coupled with Silicon Photomultiplier (SiPM) by Wavelength-Shifting (WLS) fibers, is a significant component to measure the energy and position of photons and electrons in Electromagnetic Calorimeter (ECal), a key detector of the Multi Purpose Detector (MPD) at the Nuclotron-based Ion Collider facility (NICA) in Russia. In this paper, the effect of materials adopted for absorber, reflector and WLS fiber, the length and curvature of fibers and the electrons incident position on photons transmission performance of tower is simulated based on GEANT4 software. The impact of Gaussian deviation's electron beam energy and spread of 10% in the energy range on the energy resolution of tower is also studied. Results show that the low polishing degree absorber, the high polishing degree TiO_2 reflector and the type of Y-11 WLS fibers bent with a curvature radius of greater than 11 cm can significantly improve the light output. In addition, electrons injected along the centre of tower can make photons position distribution in SiPM more uniform. The generation time of photons in scintillators and time of arrival at the SiPM both obey the Landau distribution. Finally, the energy resolution of tower can be better than 3.8%/√(E) (GeV) for a Gaussian deviation's electron beam with an average energy of 3 GeV and spread of 10% in the energy range.