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data reduction strategy in the PandaX-4T experiment

JOURNAL OF INSTRUMENTATION 2024年第5期19卷 P05029页

作者： Zhou, Yubo Chen, Xun Shanghai Jiao Tong Univ INPAC Shanghai Key Lab Particle Phys & Cosmol MOE Key Lab Particle Phys Astrophys & Cosmol Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Sch Phys & Astron Shanghai Key Lab Particle Phys & Cosmol MOE Key Lab Particle Phys Astrophys & Cosmol Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Sichuan Res Inst Chengdu 610213 Peoples R China

The PandaX-4T experiment is designed for multiple purposes, including searches for solar neutrinos, weakly interacting massive particles, and rare double beta decays of xenon isotopes. The experiment produces a huge amount of raw data that needs to be stored for related physical analyses in a wide energy range. With the upgrading of the PandaX-4T experiment, the doubled sampling rate resulted in a larger data size, which challenges both the cost and the data processing speed. To address this issue, we propose a data reduction strategy by removing the noise tail of large signals and downsampling the remaining parts of them. This strategy reduces the requirement for storage by 65% while increasing data processing speed. The influences on physical analyses on different topics at different energy regions are negligible.

关键词： data reduction methods data processing methods

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High-dimensional Bayesian likelihood normalisation for CRESST's background model

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JOURNAL OF INSTRUMENTATION 2024年第11期19卷 P11013页

作者： Angloher, G. Banik, S. Benato, G. Bento, A. Bertolini, A. Breier, R. Bucci, C. Burkhart, J. Canonica, L. D'Addabbo, A. Di Lorenzo, S. Einfalt, L. Erb, A. Feilitzsch, F. V. Fichtinger, S. Fuchs, D. Garai, A. Ghete, V. M. Gorla, P. Guillaumon, P. V. Gupta, S. Hauff, D. Jeskovsky, M. Jochum, J. Kaznacheeva, M. Kinast, A. Kluck, H. Kraus, H. Kuckuk, S. Langenkaemper, A. Mancuso, M. Marini, L. Meyer, L. Mokina, V. Nilima, A. Olmi, M. Ortmann, T. Pagliarone, C. Pattavina, L. Petricca, F. Potzel, W. Povinec, P. Proebst, F. Pucci, F. Reindl, F. Rothe, J. Schaeffner, K. Schieck, J. Schmiedmayer, D. Schoenert, S. Schwertner, C. Stahlberg, M. Stodolsky, L. Strandhagen, C. Strauss, R. Usherov, I. Wagner, F. Willers, M. Zema, V. Ferella, F. Laubenstein, M. Nisi, S. Max Planck Inst Phys & Astrophys D-80805 Munich Germany Austrian Acad Sci Inst Hochenergiephy A-1050 Vienna Austria Tech Univ Wien Atominst A-1020 Vienna Austria INFN Lab Nazl Gran Sasso I-67100 Assergi Italy Univ Coimbra Dept Fis LIBPhys UC P-3004516 Coimbra Portugal Comenius Univ Fac Math Phys & Informat Bratislava 84248 Slovakia Tech Univ Munich Phys Dept D-85747 Garching Germany Walther Meissner Inst Tieftemp Forsch D-85748 Garching Germany Eberhard Karls Univ Tubingen D-72076 Tubingen Germany Univ Oxford Dept Phys Oxford OX1 3RH England GSSI Gran Sasso Sci Inst I-67100 Laquila Italy Univ Cassino & Lazio Meridionale Dipartimento Ingn Civile & Meccan I-03043 Cassino Italy Univ Aquila Dept Phys & Chem Sci Via VetoioCOPPITO 1-2 I-67100 Laquila Italy

Using CaWO4 crystals as cryogenic calorimeters, the CRESST experiment searches for nuclear recoils caused by the scattering of potential Dark Matter particles. A reliable identification of a potential signal crucially depends on an accurate background model. In this work, we introduce an improved normalisation method for CRESST's model of electromagnetic backgrounds, which is an important technical step towards developing a more accurate background model. Spectral templates based on Geant4 simulations are normalised via a Bayesian likelihood fit to experimental background data. Contrary to our previous work, no explicit assumption of partial secular equilibrium is required a priori, which results in a more robust and versatile applicability. This new method also naturally considers the correlation between all background components. Due to these purely technical improvements, the presented method has the potential to explain up to 82.7% of the experimental background within [1 keV, 40 keV], an improvement of at most 18.6% compared to our previous method. The actual value is subject to ongoing validations of the included physics.

关键词： Analysis and statistical methods data processing methods Detector modelling and simulations I (interaction of radiation with matter, interaction of photons with matter, interaction of hadrons with matter etc) Simulation methods and programs

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Neural Networks for position reconstruction in liquid argon detectors

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JOURNAL OF INSTRUMENTATION 2024年第5期19卷 C05047页

作者： Cardenas-Montes, Miguel Santorelli, Roberto CIEMAT Avda Complutense 40 Madrid Spain

This article explores the integration of Deep Learning and Explainable Artificial Intelligence in Particle Physics, focusing on their application in position reconstruction within dual-phase liquid argon detectors for Dark Matter search. Facing challenges like pile-up scenarios, Neural Networks prove crucial for refining algorithms. This article emphasizes Deep Learning's role in addressing regression and classification problems, such as position reconstruction and particle identification, particularly in Time Projection Chambers. Explainable Artificial Intelligence emerges as pivotal in unraveling Deep Learning's complex decisions, exposing biases, and facilitating improvement cycles. Innovations like Evolutionary Neural Networks and topology-driven dataset reduction offer potential efficiency gains. The conclusion highlights challenges in analyzing massive data volumes, emphasizing the need for adaptability and ethical considerations in the pursuit of understanding Dark Matter.

关键词： data analysis data processing data processing methods Noble liquid detectors (scintillation, ionization, double-phase)

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Full-flow surface defect identification method based on spot scanning scattering for unpatterned wafer

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JOURNAL OF INSTRUMENTATION 2024年第1期19卷 P01006页

作者： Qu, Dingjun Zhou, Zuoda Li, Zhiwei Ding, Ruizhe Jin, Wei Ru, Yu Luo, Haiyan Xiong, Wei Chinese Acad Sci Anhui Inst Opt & Fine Mech Hefei Inst Phys Sci Hefei 230031 Anhui Peoples R China Univ Sci & Technol China Hefei 230026 Anhui Peoples R China Chinese Acad Sci cKey Lab Opt Calibrat & Characterizat Hefei 230031 Anhui Peoples R China

As the critical dimensions of semiconductor manufacturing processes gradually decrease, the requirements for production yield management become increasingly stringent. During the manufacturing process, there are many different types of defects, such as micron-sized particles, millimeter-sized scratches, etc. Multiple categories and different scales bring great challenges to the detection and identification of defects. This paper provides a full-flow surface defect identification method based on spot scanning scattering for unpatterned wafers. First, an adaptive threshold method with dynamic kernel windows is used to perform line-by-line scanning inspection of the wafer Mercator image. The 3 sigma decision strategy is used to avoid the impact of defects on background estimation and to improve detection sensitivity. After morphological processing, connected domain analysis is performed to obtain the defect mask, and feature information such as the shape, size, and distribution of the defect is extracted. Finally, the defect identification is performed by rules based binning, and the identified defects are converted into wafer polar coordinate image for display and analysis. In the experiments, the proposed method is used to identify micron-scale particles as well as large scratches on the millimeter scale for SiC wafers. Relative to the actual production rate requirement of 20 wafers per hour, the analysis time for a 6-inch wafer is 24.4 s, which can meet the requirement. Meanwhile, the test results illustrate the effectiveness of the method. The proposed method is recommended for early-stage defect detection and identification of unpatterned wafers.

关键词： Analysis and statistical methods data processing methods Detection of defects data acquisition circuits

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Graph Neural Network-based track finding in the LHCb vertex detector

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JOURNAL OF INSTRUMENTATION 2024年第12期19卷

作者： Correia, Anthony Giasemis, Fotis I. Garroum, Nabil Gligorov, Vladimir Vava Granado, Bertrand Sorbonne Univ LPNHE 4 Pl Jussieu Paris France CNRS IN2P3 4 Pl Jussieu Paris France Sorbonne Univ LIP6 4 Pl Jussieu Paris France CNRS 4 Pl Jussieu Paris France CERN Esplanade Particules 1 Geneva Switzerland

The next decade will see an order of magnitude increase in data collected by high-energy physics experiments, driven by the High-Luminosity LHC (HL-LHC). The reconstruction of charged particle trajectories (tracks) has always been a critical part of offline data processing pipelines. The complexity of HL-LHC data will however increasingly mandate track finding in all stages of an experiment's real-time processing. This paper presents a GNN-based track-finding pipeline tailored for the Run 3 LHCb experiment's vertex detector and benchmarks its physics performance and computational cost against existing classical algorithms on GPU architectures. A novelty of our work compared to existing GNN tracking pipelines is batched execution, in which the GPU evaluates the pipeline on hundreds of events in parallel. We evaluate the impact of neural-network quantisation on physics and computational performance, and comment on the outlook for GNN tracking algorithms for other parts of the LHCb track-finding pipeline.

关键词： data processing methods Pattern recognition, cluster finding, calibration and fitting methods Computing (architecture, farms, GRID for recording, storage, archiving, and distribution data)

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Hydra: computer vision for data quality monitoring

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JOURNAL OF INSTRUMENTATION 2024年第12期19卷 C12005页

作者： Britton, Thomas Jeske, Torri Lawrence, David Rajput, Kishansingh Thomas Jefferson Natl Accelerator Facil Newport News VA 23606 USA

Hydra, initially developed for Hall-D in 2019, is a system that utilizes computer vision to perform near real time data quality monitoring. Since then, it has been deployed across all experimental halls at Jefferson Lab, with the CLAS12 collaboration in Hall-B being the first outside of GlueX to fully utilize Hydra. The system comprises back end processes that manage the models, their inferences, and the data flow. The front-end components, accessible via web pages, allow detector experts and shift crews to view and interact with the system.

关键词： data processing methods Large detector-systems performance

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TEPX as a high-precision luminosity detector for CMS at the HL-LHC

TEPX as a high-precision luminosity detector for CMS at the ...

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Topical Workshop on Electronics for Particle Physics

作者： Haranko, M. Auzinger, G. European Org Nucl Res CERN Geneva Switzerland

The CMS BRIL project upgrades its instrumentation for the Phase-2 detector to provide high-precision luminosity and beam-induced background measurements. A part of the CMS Inner Tracker - the Tracker Endcap Pixel Detector (TEPX) - will allocate a fraction of the read-out bandwidth for luminometry. The implications of the proposed approach are highlighted. A dedicated luminosity trigger and clock distribution system is introduced and a test implementation on a demonstrator system is described. A demonstrator of the real-time on-FPGA pixel cluster counting algorithm is also described.

关键词： data processing methods Detector control systems (detector and experiment monitoring and slow-control systems architecture hardware algorithms databases) Pattern recognition cluster finding calibration and fitting methods data acquisition concepts

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Waveform processing using neural network algorithms on the front-end electronics

Waveform processing using neural network algorithms on the f...

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22nd International Workshop on Radiation Imaging Detectors

作者： Miryala, S. Mittal, S. Ren, Y. Carini, G. Deptuch, G. Fried, J. Yoo, S. Zohar, S. Brookhaven Natl Lab 98 Rochester St Upton NY 11973 USA

In a multi-channel radiation detector readout system, waveform sampling, digitization, and raw data transmission to the data acquisition system constitute a conventional processing chain. The deposited energy on the sensor is estimated by extracting peak amplitudes, area under pulse envelopes from the raw data, and starting times of signals or time of arrivals. However, such quantities can be estimated using machine learning algorithms on the front-end Application-Specific Integrated Circuits (ASICs), often termed as "edge computing". Edge computation offers enormous benefits, especially when the analytical forms are not fully known or the registered waveform suffers from noise and imperfections of practical implementations. In this work, we aim to predict peak amplitude from a single waveform snippet whose rising and falling edges containing only 3 to 4 samples. We thoroughly studied two well-accepted neural network algorithms, Multi-Layer Perceptron (MLP) and Convolutional Neural Network (CNN) by varying their model sizes. To better fit front-end electronics, neural network model reduction techniques, such as network pruning methods and variable-bit quantization approaches, were also studied. By combining pruning and quantization, our best performing model has the size of 1.5 KB, reduced from 16.6 KB of its full model counterpart. It can reach mean absolute error of 0.034 comparing to that of a naive baseline of 0.135. Such parameter-efficient and predictive neural network models established feasibility and practicality of their deployment on front-end ASICs.

关键词： data processing methods data reduction methods Front-end electronics for detector readout Simulation methods and programs

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Multi-scale deep residual shrinkage networks with a hybrid attention mechanism for rolling bearing fault diagnosis

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JOURNAL OF INSTRUMENTATION 2024年第5期19卷 P05015页

作者： Zhang, Xinliang Wang, Yanqi Wei, Shengqiang Zhou, Yitian Jia, Lijie Henan Polytech Univ Sch Elect Engn & Automat Henan Int Joint Lab Direct Drive & Control Intelli Jiaozuo 454003 Peoples R China Anhui Inst Informat Technol Wuhu 241000 Anhui Peoples R China Zhoushan Yangwangnaxin Technol Co Ltd Zhoushan 3161041 Zhejiang Peoples R China

The fault diagnosis of rolling bearings based on deep networks is hindered by the unexpected noise involved with accessible vibration signals and global information abatement in deepened networks. To combat the degradation, a multi -scale deep residual shrinkage network with a hybrid attention mechanism (MH-DRSN) is proposed in this paper. First, a spatial domain attention mechanism is introduced into the residual shrinkage module to represent the distance dependence of the feature maps. Then, a hybrid attention mechanism considering both the inner -channeled and cross -channeled characteristics is constructed. Through the comprehensive evaluation of the feature map, it provides a soft threshold for the activation function and realizes the feature -map selection adaptively. Second, the dilated convolution with different dilation rates is implemented for multi -scale context information extraction. Through the feature combination of the DRSN and the dilated convolution, the global information of the rolling bearing fault is strengthened and preserved as the fault diagnosis network is deepened. Finally, the performance of the proposed fault -diagnosis model is validated on the dataset from Case Western Reserve University (CWRU). The experimental results show that, compared with common convolution neural networks, the proposed neural diagnosis model provides a higher identification accuracy and better robustness under noise interference.

关键词： Detection of defects Digital signal processing (DSP) data processing methods Overall mechanics design (support structures and materials, vibration analysis etc)

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Optimization of the magnetic field in the SK1000 central region model magnet

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JOURNAL OF INSTRUMENTATION 2024年第12期19卷 P12023页

作者： Wang, J. Yao, Q. G. Dou, G. L. Lyu, M. B. Zhao, L. X. Zhang, X. Wei, Z. Sun, L. T. Zhao, H. W. Lanzhou Univ LZU Lanzhou 730000 Gansu Peoples R China Chinese Acad Sci Inst Modern Phys IMP Lanzhou 730000 Peoples R China Univ Chinese Acad Sci UCAS Beijing 100000 Peoples R China Gansu Isotope Lab Lanzhou 730000 Peoples R China

This study presents a design scheme for a central region model magnet, based on a high-intensity compact cyclotron. The objective is to ensure the long-term stable production of therapeutic radionuclides such as 211At and 225Ac, while maintaining reasonable construction and operational costs. In-depth research into the design principles and core parameters of the CRM (Central Region Model) cyclotron magnet system, guided by the concept of isochronous magnetic field design, enabled the optimization of the magnet's dimensions and magnetic field distribution. The optimization results show that the magnet system meets the acceleration and focusing requirements of the particle beam, effectively prevents particles from entering dangerous resonance regions, and aligns with the theoretical requirements of isochronous magnetic fields. This method keeps the error between the calculated magnetic field and the theoretical design field within 5 Gauss, meeting the expected physical design standards.

关键词： Accelerator modelling and simulations (multi-particle dynamics single-particle dynamics) data processing methods Acceleration cavities and superconducting magnets (high-temperature superconductor radiation hardened magnets normal-conducting permanent magnet devices wigglers and undulators)

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