Muons can be identified with high efficiency and purity and reconstructed with high precision is a detector with a dual readout calorimeter and a dual solenoid to return the flux without iron. We shown CERN test beam data for the calorimeter and calculations for the magnetic fields and the track reconstruction. For isolated tracks, the rejection of pions against muons ranges from $10^3$ at 20 GeV/c to $10^5$ at 300 GeV/c.
This work reports a cost-effective, multi-parameter readout and data-acquisition system for a muon scattering tomography system based on Resistive Plate Chambers (RPCs). Initial test measurements with a prototype Resistive Plate Chamber were performed using a low-cost FPGA coupled to the NINO ASIC for the event selection and handling data. The Time over-Threshold (TOT) property of NINO ASICs has been used to achieve better position information and achieve precise tracking capability. In our test setup, we try to build an imaging setup using a single RPC and lead block, which covers some areas …
Muon identification is of paramount importance for the physics programme of LHCb. In the upgrade phase, starting from Run 3 of the LHC, the trigger of the experiment will be solely based on software. The luminosity increase to $2\times10^{33}$ cm$^{-2}$s$^{-1}$ will require an improvement of the muon identification criteria, aiming at performances equal or better than those of Run 2, but in a much more challenging environment. In this paper, two new muon identification algorithms developed in view of the LHCb upgrade are presented, and their performance in terms of signal efficiency versus bac…
The use of muon scattering tomography for the non-invasive characterisation of nuclear waste is well established. We report here on the application of a combination of feature discriminators and multivariate analysis techniques to locate and identify materials in nuclear waste drums. After successful training and optimisation of the algorithms they are then tested on a range of material configurations to assess the system's performance and limitations. The system is able to correctly identify uranium, iron and lead objects on a ~few \text{cm} scale. The system's sensitivity to small uranium ob…
Nowadays the implementation of artificial neural networks in high-energy physics has obtained excellent results on improving signal detection. In this work we propose to use neural networks (NNs) for event discrimination in HAWC. This observatory is a water Cherenkov gamma-ray detector that in recent years has implemented algorithms to identify horizontal muon tracks. However, these algorithms are not very efficient. In this work we describe the implementation of three NNs: two based on image classification and one based on object detection. Using these algorithms we obtain an increase in the …
I present an application of a convolutional neural network (CNN) to separate muons and pions in the Belle II electromagnetic calorimeter (ECL). The ECL is designed to measure the energy deposited by charged and neutral particles. It also provides important contributions to the particle identification (PID) system. Identification of low-momenta muons and pions in the ECL is crucial if they do not reach the outer muon detector. Track-seeded cluster energy images provide the maximal possible information. The shape of the energy depositions for muons and pions in the crystals around an extrapolate…
Improving Muon-Scattering Material Identification via Coarse Momentum Encoding and Unsupervised Domain Adaptation
Hefei University of Technology（合肥工业大学）；Institute of Modern Physics, Chinese Academy of Sciences（中国科学院近代物理研究所）；Frontiers Science Center for Rare Isotopes, Lanzhou University（兰州大学稀有同位素前沿科学中心）；Advanced Energy Science and Technology Guangdong Laboratory（广东省实验室（先进能源科学与技术））；University of Chinese Academy of Sciences（中国科学院大学）；State Key Laboratory of Heavy Ion Science and Technology, Institute of Modern Physics, Chinese Academy of Sciences（中国科学院近代物理研究所重离子科学和技术国家重点实验室）；China Academy of Engineering Physics（中国工程物理研究院）
The muon tagging is an essential tool to distinguish between gamma and hadron-induced showers in wide field-of-view gamma-ray observatories. In this work, it is shown that an efficient muon tagging (and counting) can be achieved using a water Cherenkov detector with a reduced water volume and 4 PMTs, provided that the PMT signal spatial and time patterns are interpreted by an analysis based on Machine Learning (ML). The developed analysis has been tested for different shower and array configurations. The output of the ML analysis, the probability of having a muon in the WCD station, has been u…
As a novel approach on visual photon imaging by a single photon sensitive camera and PMTs, this work is trying to measure and identify muon tracks from the 2-D images of CsI(Tl) crystal (scintillator detectors). It is possible that muon tracks can be seen directly with a good signal-to-noise ratio neither with further amplification nor external light, which provides an evolution method for particle measurement in the photon-starved regime of scintillation detectors. The setup of the crystal and camera testing system and the identification algorithm of muon track will be discussed in detail inc…
Laboratório de Instrumentação e Física Experimental de Partículas (LIP)（粒子实验物理与仪器实验室）；Instituto Superior Técnico (IST), Universidade de Lisboa（里斯本大学高等理工学院）；University of Granada（格拉纳达大学）
This paper presents several approaches to deal with the problem of identifying muons in a water Cherenkov detector with a reduced water volume and 4 PMTs. Different perspectives of information representation are used and new features are engineered using the specific domain knowledge. As results show, these new features, in combination with the convolutional layers, are able to achieve a good performance avoiding overfitting and being able to generalise properly for the test set. The results also prove that the combination of state-of-the-art Machine Learning analysis techniques and water Cher…
NuMI离轴现象（英语：NuMI Off-Axis v Appearance，缩写：NOvA）实验是一个长基线中微子振荡实验，使用的是费米实验室的NuMI光束线产生的中微子源。该实验将使用两个功能相同的塑料液体闪烁体填充探测器研究μ子中微子到电子中微子的振荡，该探测器放置在离轴14毫弧度的NuMI光束上。带电中微子相互作用将被用来观察中微子味从识别的最终状态轻子。μ子带电粒子径迹的重建在探测器对准和校准的短期目标以及通过识别μ子带电电流事件的长期振荡分析中起着重要作用。提出了一种基于卡尔曼滤波的μ子径迹识别和径迹拟合的初步方法。
Full Characterization of a Mock Nuclear Waste Barrel with Muon Tomography using Micromegas Detectors
CEA/Irfu, Université Paris-Saclay（法国原子能委员会/核研究与物理技术研究所，巴黎萨克雷大学）；Centro de Astropartículas y Física de Altas Energías (CAPA), Universidad de Zaragoza（高能天体粒子中心，萨拉戈萨大学）
