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A software system for rapid analysis of plastics using laser-induced breakdown spectroscopy

JOURNAL OF INSTRUMENTATION 2020年第4期15卷

作者： Liu, K. Tian, D. Yue, X. Yang, G. Jilin Univ Coll Instrumentat & Elect Engn 938 Ximinzhu Ave Changchun 130061 Peoples R China

With the extensive use of plastic products, the recycling and reuse of plastics raises more concerns. Laser-induced breakdown spectroscopy (LIBS) has become a powerful technology for plastic analysis. To meet different analytical requirements, LIBS control system and analysis software was being continuously developed. However, existing software system only can realize the capability of LIBS system control or data analysis (peak detection or material identification). To improve the efficiency of LIBS instrument for plastic analysis, an integrated and full software is needed. In this work, analyis & test software was developed for rapid plastic analysis, which consists of LIBS control platform, plastic database, and analysis module which contains peak detection, qualitative and quantitative analysis. The software performance was validated, which provides an automatic processing for plastic analysis. Design approach of the software can be used for the development of similar instrument software and other samples analysis.

关键词： data processing methods Pattern recognition cluster finding calibration and fitting methods Plasma generation (laser-produced, RF, x ray-produced) Software architectures (event data models, frameworks and databases)

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Novel tracking approach based on fully-unsupervised disentanglement of the geometrical factors of variation

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JOURNAL OF INSTRUMENTATION 2020年第3期15卷

作者： Vladymyrov, M. Ariga, A. Univ Bern Albert Einstein Ctr Fundamental Phys Lab High Energy Phys Sidlerstr 5 CH-3012 Bern Switzerland Univ Bern Theodor Kocher Inst Freiestr 1 CH-3012 Bern Switzerland Univ Bern Sci IT Support Sidlerstr 5 CH-3012 Bern Switzerland

Efficient tracking algorithms are a crucial part of particle tracking detectors. While a lot of work has been done in designing a plethora of algorithms, these usually require tedious tuning for each use case. (Weakly) supervised Machine Learning-based approaches can leverage the actual raw data for maximal performance. Yet in realistic scenarios, sufficient high-quality labeled data is not available. While training might be performed on simulated data, the reproduction of realistic signal and noise in the detector requires substantial effort, compromising this approach. Here we propose a novel, fully unsupervised, approach to track reconstruction. The introduced model for learning to disentangle the factors of variation in a geometrically meaningful way employs geometrical space invariances. We train it through constraints on the equivariance between the image space and the latent representation in a Deep Convolutional Autoencoder. Using experimental results on synthetic data we show that a combination of different space transformations is required for meaningful disentanglement of factors of variation. We also demonstrate the performance of our model on real data from tracking detectors.

关键词： data processing methods Particle tracking detectors Pattern recognition, cluster, finding, calibration and fitting methods

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Coincidence time resolution investigation of BaF2-based H6610 detectors for a digital positron annihilation lifetime spectrometer

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JOURNAL OF INSTRUMENTATION 2020年第6期15卷

作者： Ye, R. Zhao, Q. H. Wang, H. B. Gu, B. C. Pan, Z. W. Liu, J. D. Ye, B. J. Univ Sci & Technol China State Key Lab Particle Detect & Elect Hefei 230026 Peoples R China

BaF2-based detectors have been used in many fields requiring fast timing, especially in positron annihilation lifetime (PAL) spectroscopy. The performance of a PAL spectrometer is closely correlated with the time response of BaF2-based detectors. Therefore, it is important to select scintillation detectors with excellent time resolution. In this work, the coincidence time resolution of BaF2-based H6610 detectors was investigated using a digital oscilloscope. The time response of detectors has been optimized by tuning the supply voltages, the sampling rate of the oscilloscope, and digital fraction constants, achieving a conicidence time resolution of about 162 ps for the 0.511 MeV annihilation gamma-ray pairs and 108 ps for the Co-60 cascade gamma-rays, respectively. Furthermore, a digital PAL spectrometer composed of two BaF2-based H6610 detectors and an oscilloscope was developed with a time resolution of around 130 ps, much better than most digital PAL spectrometers.

关键词： Gamma detectors (scintillators, CZT, HPGe, HgI etc) Timing detectors data processing methods Detection of defects

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Multiple time-over-threshold readout electronics for fast timing and energy resolving in a SiPM-based positron annihilation lifetime spectrometer

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JOURNAL OF INSTRUMENTATION 2020年第11期15卷

作者： Wang, H. B. Liu, J. D. Zhang, H. J. Liang, H. Ye, B. J. Univ Sci & Technol China State Key Lab Particle Detect & Elect Hefei 230026 Peoples R China

Fast timing and energy resolving capabilities are crucial for a silicon-photomultiplier-based positron annihilation lifetime (SiPM-PAL) spectrometer. Time-over-threshold (ToT) offers an attractive method for combining timing and energy encoding with the advantage of simplicity in electronics implementation and SiPM readout. However, single ToT is not appropriate for a SiPM-PAL spectrometer because of its poor energy linearity and limited energy range. In this work, a multi-ToT readout circuit for a SiPM-PAL spectrometer is developed. Each readout channel uses only a pre-amplifier and a comparator for timing, three comparators for energy resolving, resistors, and capacitors. A simple pulse area estimation method using multi-ToT pulse widths and fixed threshold voltages for energy resolving is presented and evaluated. The energy performance and pulse area integral nonlinearity (INL) are investigated using Na-22, Co-60, and Cs-137 gamma sources with a SiPM-based scintillation detector. For pulse area spectra using the proposed method, multiToT (triple and dual threshold cases) resolves gamma energies much better than does single ToT. Using triple-ToT, the pulse area linearity in INL in the energy range of 511-1333 keV is superior than 4.21%, which is at least 25% better than that with dual-ToT. Excellent coincidence resolving times of 161.5 +/- 1.4 ps and 162.7 +/- 1.3 ps in full width at half maximum are achieved using tripleToT and dual-ToT, respectively, for 3 mm x 3 mm x 5 mm lutetium-fine-silicate scintillators. The quantitative energy and timing results show that the multi-ToT circuit is suitable for the readout electronics of a SiPM-PAL spectrometer.

关键词： Front-end electronics for detector readout data processing methods Photon detectors for UV, visible and IR photons (solid-state) (PIN diodes, APDs, Si-PMTs, G-APDs, CCDs, EBCCDs, EMCCDs, CMOS imagers, etc)

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Parametrizing the detector response with neural networks

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JOURNAL OF INSTRUMENTATION 2020年第1期15卷

作者： Cheong, S. Cukierman, A. Nachman, B. Safdari, M. Schwartzman, A. SLAC Natl Accelerator Lab Elementary Particle Phys Div 2575 Sand Hill Rd Menlo Pk CA 94025 USA Stanford Univ Dept Phys 450 Jane Stanford Way Stanford CA 94305 USA Lawrence Berkeley Natl Lab Div Phys 1 Cyclotron Rd Berkeley CA USA

In high energy physics, characterizing the response of a detector to radiation is one of the most important and fundamental experimental tasks. In many cases, this task is accomplished by parameterizing summary statistics of the full detector response probability density. The parameterized detector response can then be used for calibration as well as for directly improving physics analysis sensitivity. This paper discusses how to parameterize summary statistics of the detector response using neural networks. In particular, neural networks are powerful tools for incorporating multidimensional data and the loss function used during training determines which summary statistic is learned. One common summary statistic that has not been combined with deep learning (as far as the authors are aware) is the mode. A neural network-based approach to mode learning is proposed and empirically demonstrated in the context of high energy jet calibrations. Altogether, the neural network-based toolkit for detector response parameterization can enhance the utility of data collected at high energy physics experiments and beyond.

关键词： Analysis and statistical methods data processing methods Large detector-systems performance Pattern recognition cluster finding calibration and fitting methods

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A spectral clustering based method for automatic plasma pattern identification in electron cyclotron emission imaging on EAST Tokamak

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JOURNAL OF INSTRUMENTATION 2020年第7期15卷

作者： Zhu, Y. Xiao, J. Lan, T. Xie, J. Univ Sci & Technol China Sch Phys Sci Dept Engn & Appl Phys 96 Jinzhai Rd Hefei Peoples R China

Electron cyclotron emission imaging (ECEI) diagnostic system of Experimental Advanced Superconducting Tokamak (EAST) can provide two-dimensional electron temperature fluctuation on the cross section of the EAST Tokamak. A major task in ECEI data analysis is to recognize patterns in ECEI signals. In this paper, we present a machine learning method to automatically identify the temporal and spacial location of plasma patterns inside ECEI data of EAST tokamak. The method is based on spectral clustering of ECEI signals of different channels. It is applied to ECEI data of 5 discharges obtained from EAST tokamak and the results can verify the effectiveness of our method. Theoretically it can be applied to recognize patterns in many temporal and spacial diagnostics.

关键词： Pattern recognition, cluster finding, calibration and fitting methods Plasma diagnostics - interferometry, spectroscopy and imaging data processing methods

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Evaluation of dynamic spectrum extraction method based on salami slicing method

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INFRARED PHYSICS & TECHNOLOGY 2020年 111卷 103551-103551页

作者： Xiao, Qiuyue Li, Gang Yan, Wenjuan He, Guoquan Lin, Ling Tianjin Univ State Key Lab Precis Measurement Technol & Instru Tianjin 300072 Peoples R China Tianjin Univ China & Tianjin Key Lab Biomed Detecting Tech & I Tianjin 300072 Peoples R China Yangtze Normal Univ Sch Elect Informat Engn Chongqing 408100 Peoples R China

With the rapid development of computer technology, a variety of data processing methods are used to process more and more data. The evaluation of the quality of the data and the processing methods is an urgent problem to be solved. In this paper, the dynamic spectrum for non-invasive blood component detection is used as the research object. A quality evaluation method based on salami slicing method is proposed. This evaluation method is applied to evaluate three dynamic spectrum extraction methods including optimizing differential extraction method, single-trial extraction method and differential extraction method, and their corresponding extracted dynamic spectrum. The evaluation results show that, the extraction accuracy of optimizing differential extraction method is the highest, single-trial extraction method is the second, and differential extraction method is the last. The evaluation results of three dynamic spectrum extraction methods verify the feasibility of the salami slicing evaluation method. This method can also be used to evaluate other data and data processing methods, and has high application value and broad application prospect.

关键词： data processing methods Non-invasive blood component detection Quality evaluation Salami slicing method

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Wasserstein distance based deep multi-feature adversarial transfer diagnosis approach under variable working conditions

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JOURNAL OF INSTRUMENTATION 2020年第6期15卷

作者： She, D. Peng, N. Jia, M. Pecht, M. G. Southeast Univ Sch Mech Engn 79 Suyuan Ave Nanjing 211189 Peoples R China Univ Maryland Ctr Adv Life Cycle Engn CALCE 1104 Engn Lab Bldg College Pk MD 20742 USA

Intelligent mechanical fault diagnosis is a crucial measure to ensure the safe operation of equipment. To solve the problem that network features is not fully utilized in the adversarial transfer learning, this paper develops a Wasserstein distance based deep multi-feature adversarial (WDDMA) transfer diagnosis approach under variable working conditions. Domain adaptation is realized by adapting multi-feature in adversarial training and reducing the Wasserstein distance between the two domains after the discriminator network. The multi-feature are adapted at the same time to improve the recognition ability of the transfer network to the target domain. Experimental datasets under variable working conditions support the value of our approach.

关键词： data processing methods Manufacturing Pattern recognition cluster finding calibration and fitting methods Control and monitor systems online

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Fast inference of Boosted Decision Trees in FPGAs for particle physics

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JOURNAL OF INSTRUMENTATION 2020年第5期15卷

作者： Summers, S. Di Guglielmo, G. Duarte, J. Harris, P. Hoang, D. Jindariani, S. Kreinar, E. Loncar, V Ngadiuba, J. Pierini, M. Rankin, D. Tran, N. Wu, Z. CERN Esplanade Particules 1 CH-1211 Geneva 23 Switzerland Columbia Univ Dept Comp Sci 500 West 120 St New York NY 10027 USA Univ Calif San Diego Dept Phys 9500 Gilman Dr La Jolla CA 92093 USA MIT Dept Phys 77 Massachusetts Ave Cambridge MA 02139 USA Rhodes Coll Dept Phys 2000 North Pkwy Memphis TN 38112 USA Fermilab Natl Accelerator Lab POB 500 Batavia IL 60510 USA HawkEye360 Herndon VA 20170 USA Inst Phys Belgrade Pregrevica 118 Belgrade Serbia Univ Illinois Dept Phys W Taylor St Chicago IL 60607 USA

We describe the implementation of Boosted Decision Trees in the hls4ml library, which allows the translation of a trained model into FPGA firmware through an automated conversion process. Thanks to its fully on-chip implementation, hls4ml performs inference of Boosted Decision Tree models with extremely low latency. With a typical latency less than 100 ns, this solution is suitable for FPGA-based real-time processing, such as in the Level-1 Trigger system of a collider experiment. These developments open up prospects for physicists to deploy BDTs in FPGAs for identifying the origin of jets, better reconstructing the energies of muons, and enabling better selection of rare signal processes.

关键词： Analysis and statistical methods data processing methods Digital electronic circuits Trigger algorithms

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Detection of true Gaussian shaped pulses at high count rates

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JOURNAL OF INSTRUMENTATION 2020年第6期15卷

作者： Kantor, M. Yu Sidorov, A., V Ioffe Inst Polytekhnicheskaya 26 St Petersburg Russia

A new true Gaussian digital shaper of detector pulses is analised for applications with soft X-ray spectrometers at the highest count rates. The true Gaussian shaper allows for shaping detector pulses into a symmetrical form which width can be considerably less than the rise time of the input pulses. Therefore, the dead time of the true Gaussian shaper can be noticeably reduced in regards to that of trapezoidal shaper. Less dead time provides a higher count rate of the true Gaussian pulses. The true Gaussian shaper is compared to the standard trapezoidal shaper in terms of count rate, amplitude resolution and biasing the output shaped pulses in a wide range of the input count rates. The maximal output count rate of the true Gaussian shaper has been found to exceed the rate of the trapezoidal shapers in several times. The true Gaussian shaper provides biasing-free measurements of pulse amplitudes with better resolution than that provided by the trapezoidal shapers at high count rates. The tests have been performed by modeling the output signals of silicon drift detector (SDD) H7 VITUS equipped with AXAS-D pre-amplifier developed by KETEK GmbH for measurements of soft X-ray spectra at high count rates.

关键词： data processing methods Electronic detector readout concepts (solid-state) Digital signal processing (DSP) Nuclear instruments and methods for hot plasma diagnostics

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