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Optimizing deep learning-based piled-up pulse height correction method for high radiation-field application

JOURNAL OF INSTRUMENTATION 2023年第12期18卷 C12002页

作者： Kim, W. Ko, K. Lee, S. Park, J. Song, G. Lim, K. Cho, G. Korea Adv Inst Sci & Technol Dept Nucl & Quantum Engn 291 Daehak Ro Daejeon South Korea Sejong Univ Dept Quantum & Nucl Engn 209 Neungdong Ro Seoul South Korea

In high-radiation environments, measured pile-up pulses can lead to unavoidable issues such as total count loss and spectrum distortion. Additionally, the recording of large volumes of data within a short period makes real-time processing difficult. In this study, a deep learning-based pulse height estimation (PHE) method was optimized to perform pile-up signal correction in high-radiation fields. First, we adopted a previous deep-learning-based PHE method that allows for fast correction without being restricted to specific detectors. However, the peak-finding method was slightly modified to improve the count restoration rate. Moreover, the input data length of the deep learning model was optimized for convolutional neural networks (CNN) and deep neural networks (DNN) to achieve the maximum correction performance using minimal input data. A series of single pulses was experimentally obtained from a LaBr3 detector with a short decay time to prepare a dataset for training the deep learning models. The pile-up signals were generated by randomly synthesizing single pulses. Samples around their peaks were sliced using the peak-finding method and used as input data for the deep learning models. As a result of the optimization, the modified peak-finding method improved the count restoration rate compared to the previous method by effectively detecting the peaks of tail pile-up, and peak pile-up pulses. Furthermore, the input data length and region were optimized based on the performance evaluation of each deep learning model. Despite having a simpler architecture than the CNN model, the DNN model demonstrated excellent PHE performance. The results of this study showed the efficient and practical considerations necessary for applying pile-up signal correction in high-radiation fields.

关键词： Pattern recognition cluster finding calibration and fitting methods data processing methods Digital signal processing (DSP)

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Geant4 simulation study of low-Z material detection using muon tomography

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JOURNAL OF INSTRUMENTATION 2023年第12期18卷 C12014页

作者： Georgadze, Anzori Sh. Kudryavtsev, Vitaly A. Univ Tartu Inst Phys W Ostwaldi 1 EE-50411 Tartu Estonia Natl Acad Sci Ukraine Inst Nucl Res Prospekt Nauky 47 UA-03028 Kiev Ukraine Univ Sheffield Dept Phys & Astron Hounsfield Rd Sheffield S3 7RH England

Traditional X-ray scanning systems for cargo use ionising radiation which can be harmful to operators and the environment and requires shielding. Fully passive muon tomography is a promising alternative or a complementary approach to X-ray scanners. Muon tomography is a non-invasive technique that uses naturally occurring cosmic-ray muons and their scattering in various materials to create images of cargo in trucks or containers without applying ionising radiation. Muons are high-energy particles that are produced when primary cosmic rays collide with the Earth's atmosphere. These muons can penetrate through thick materials, such as concrete or metal, and are therefore useful for detecting hidden objects, including contraband. Muon tomography is expected to be used for detection of a wide range of materials, including metals, plastics, and organic materials like drugs or cigarettes, as well as weapons and explosives. In this work we have used the GEANT4 toolkit to simulate the performance of muon tomography in identifying the contraband hidden inside the legal cargo. We have used the Point of Closest Approach (PoCA) reconstruction algorithm to reconstruct the three-dimensional image of a loaded truck.

关键词： data processing methods Detector modelling and simulations I (interaction of radiation with matter, interaction of photons with matter, interaction of hadrons with matter, etc) Image filtering Particle tracking detectors

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Deep neural network uncertainty quantification for LArTPC reconstruction

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JOURNAL OF INSTRUMENTATION 2023年第12期18卷 P12013页

作者： Koh, D. Mishra, A. Terao, K. Stanford Univ 450 Serra Mall Stanford CA 94305 USA SLAC Natl Accelerator Lab 2575 Sand Hill Rd Menlo Pk CA USA

We evaluate uncertainty quantification (UQ) methods for deep learning applied to liquid argon time projection chamber (LArTPC) physics analysis tasks. As deep learning applications enter widespread usage among physics data analysis, neural networks with reliable estimates of prediction uncertainty and robust performance against overconfidence and out-of-distribution (OOD) samples are critical for their full deployment in analyzing experimental data. While numerous UQ methods have been tested on simple datasets, performance evaluations for more complex tasks and datasets are scarce. We assess the application of selected deep learning UQ methods on the task of particle classification using the PiLArNet [1] monte carlo 3D LArTPC point cloud dataset. We observe that UQ methods not only allow for better rejection of prediction mistakes and OOD detection, but also generally achieve higher overall accuracy across different task settings. We assess the precision of uncertainty quantification using different evaluation metrics, such as distributional separation of prediction entropy across correctly and incorrectly identified samples, receiver operating characteristic curves (ROCs), and expected calibration error from observed empirical accuracy. We conclude that ensembling methods can obtain well calibrated classification probabilities and generally perform better than other existing methods in deep learning UQ literature.

关键词： Analysis and statistical methods data processing methods Pattern recognition cluster finding calibration and fitting methods Performance of High Energy Physics Detectors

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Power line interference and baseline wander removal from ECG signals using local characteristic decomposition

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JOURNAL OF INSTRUMENTATION 2022年第6期17卷 P06025页

作者： Dorostghol, A. Maghsoudpour, A. Ghaffari, A. Nikkhah-Bahrami, M. Islamic Azad Univ Dept Mech Engn Sci & Res Branch Tehran Iran KN Toosi Univ Technol Dept Mech Engn 19 Pardis StMollasadra AveVanak Sq Tehran Iran

Noise detection and removal are crucial in ECG processing. In this study, a method based on local characteristic decomposition (LCD) algorithm for denoising the ECG signals is discussed. In particular, specific techniques for the removal of baseline wandering (BW) and of power line interference (PLI), which are two common noises in ECG, are proposed and tested against real data. Simulation results show that the proposed method is more reliable at various signals to noise ratios. These results also indicate that the proposed method comparing to some conventional denoising methods such as wavelet, Butterworth, and empirical mode decomposition (EMD), leads to a higher quality denoising approach.

关键词： Bioinstrumentation data processing methods Image filtering Image processing

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Invisible barcode method base on NDT photoacoustic imaging

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JOURNAL OF INSTRUMENTATION 2022年第2期17卷 P02006页

作者： Setiawan, A. Mitrayana Univ Kristen Satya Wacana Dept Phys Salatiga Indonesia Univ Kristen Satya Wacana Study Ctr Multidisciplinary Appl Res & Technol Salatiga Indonesia Univ Gadjah Mada Dept Phys Yogyakarta Indonesia

The use of barcode technology has spread in many fields since it is cheaper and easier to use. To date, the barcode technology is applied by placing a code on a label surface. This makes access easier but also increases security risks. Security breaches can occur either due to physical damage to the code or code hijacking for inappropriate purposes. This paper reports the development of the subsurface barcode coding method by utilizing Non-Destructive Testing (NDT) photoacoustic imaging. Referring to the Rosencwig-Gersho theory, photoacoustics can image the subsurface conditions by adjusting the thermal conductivity length. In this experiment, a number of cavities representing barcodes were embedded in a metal object. The experimental results show that this method can decode the 3-digit Flattermarken code embedded in a 1 mm thick metal object again. The success of this new method is expected to open up more opportunities for barcodes that are more secure and resistant to tampering.

关键词： data processing methods Detection of defects data acquisition circuits Detector design and construction technologies and materials

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A new technique to enhance the position resolution of large area plastic scinitillators to reconstruct the cosmic muon tracks

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JOURNAL OF INSTRUMENTATION 2022年第2期17卷 P02036页

作者： Sehgal, R. Dey, R. Behera, S. P. Netrakanti, P. K. Mishra, D. K. Mulmule, D. Jha, V Pant, L. M. Bhabha Atom Res Ctr Nucl Phys Div Mumbai 400085 Maharashtra India Homi Bhabha Natl Inst Mumbai 400094 Maharashtra India

In this paper, we present a study to use thick plastic scintillators to reconstruct the cosmic muon tracks, that can be used for the applications like Muon Tomography. At Bhabha Atomic Research Centre (BARC), India, a plastic scintillator array - 'ISMRAN (Indian Scintillator Matrix for Reactor Anti-Neutrinos),' with a total weight of 1.0 ton has been configured for neutrino physics study. Using the ISMRAN scintillators matrix, we present a technique of the position calibration of thick plastic scintillators using cosmic muons as a probe. The position resolution obtained from the cosmic muons based calibration method is compared with the one obtained from the traditional calibration method using the radioactive source. Finally, the accuracy of reconstructed cosmic muon tracks from the two position calibration techniques is compared using the chi(2)/ndf distribution of the fitted cosmic muon tracks.

关键词： data processing methods Pattern recognition, cluster finding, calibration and fitting methods Scintillators, scintillation and light emission processes (solid, gas and liquid scintillators) Detector modelling and simulations I (interaction of radiation with matter, interaction of photons with matter, interaction of hadrons with matter, etc)

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FPGA-based real-time data processing for accelerating reconstruction at LHCb

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JOURNAL OF INSTRUMENTATION 2022年第4期17卷 C04011页

作者： Lazzari, F. Baldini, W. Bassi, G. Contu, A. Dorigo, M. Fantechi, R. Giambastiani, L. Morello, M. J. Punzi, G. Sticchi, M. Tuci, G. Ist Nazl Fis Nucl Sez Pisa Largo B Pontecorvo 3 Pisa Italy Univ Siena Via Banchi Sotto 55 Siena Italy Ist Nazl Fis Nucl Sez Ferrara Via Saragat 1 Ferrara Italy Univ Ferrara Via Ludov Ariosto 35 Ferrara Italy Scuola Normale Super Pisa Piazza Cavalieri 7 Pisa Italy Ist Nazl Fis Nucl Sez Cagliari SP Sestu Monserrato Italy Ist Nazl Fis Nucl Sez Trieste Via Valerio 2 Trieste Italy Univ Pisa Lungarno Pacinotti 43 Pisa Italy Ist Nazl Fis Nucl Sez Padova Via Marzolo 8 Padua Italy Univ Padua Via 8 Febbraio 2 Padua Italy Univ Chinese Acad Sci 19 A Yuquan Rd Beijing Peoples R China

In run-3, beginning in 2022, the LHCb software trigger will start reconstructing events at the LHC average crossing rate of 30 MHz. Within the upgraded DAQ system, LHCb established a testbed for new heterogeneous computing solutions for real-time event reconstruction, in view of future runs at even higher luminosities. One such solution is a highly-parallelized custom tracking processor ("Artificial Retina"), implemented in state of the art FPGA devices connected by fast serial links. We describe the status of a realistic prototype for the reconstruction of pixel tracking detectors that will run on real data during run-3.

关键词： Pattern recognition cluster finding calibration and fitting methods data processing methods Trigger concepts and systems (hardware and software)

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A multi-parameter discrimination digital positron annihilation lifetime spectrometer using a fast digital oscilloscope

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NUCLEAR INSTRUMENTS & methods IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT 2022年 1023卷

作者： Zhao, Q. H. Ye, R. Wang, H. B. Cong, L. H. Liu, J. D. Zhang, H. J. Ye, B. J. Univ Sci & Technol China State Key Lab Particle Detect & Elect Hefei 230026 Peoples R China Univ Sci & Technol China Hefei Natl Lab Phys Sci Microscale Hefei 230026 Peoples R China

Positron annihilation lifetime (PAL) spectroscopy is widely used in the characterization of material microstructures. It has a very high sensitivity to the material defects. This paper describes how we developed a digital PAL spectrometer with two barium fluoride (BaF2) detectors and reading the data with a fast digital oscilloscope. We achieved better time performance for the digital PAL spectrometer using a multi-parameter discrimination method and various pulse smoothing algorithms. With two BaF2 detectors in the coincidence mode, we achieved a time resolution of 160 ps for 0.511 MeV annihilation gamma rays. The multi-parameter discrimination method implemented can remove incorrect events more effectively than the conventional energy discrimination method. With this enhancement in event discrimination, the digital PAL spectrometer's time performance has improved, which results in more accurate PAL characterization. In a lifetime measurement, the PAL spectrometer's time resolution is about 130 ps, which is much better than most PAL spectrometers. With the multi-parameter discrimination method, the resolution component from the incorrect events decreases drastically, and the leading edge distortion of the PAL spectrum is significantly improved.

关键词： Positron annihilation lifetime spectrometer data processing methods BaF2 Timing detectors Pulse discrimination

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Passive acoustic monitoring of cetaceans with KM3NeT acoustic receivers

Passive acoustic monitoring of cetaceans with KM3NeT acousti...

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Conference on Very Large Volume Neutrino Telescope

作者： Guidi, C. Bou-Cabo, M. Lara, G. Ist Nazl Fis Nucl Genoa Italy Inst Espanol Oceanog Unidad Mixta IEO UPV Gandia Spain

KM3NeT (Cubic Kilometer Neutrino Telescope) is a research infrastructure that comprises two underwater neutrino detectors located at different sites in the Mediterranean Sea: KM3NeT-Fr (ORCA) (offshore the coast of Toulon, France, at a depth of around 2500 m) and KM3NeT-It (ARCA) (off Capo Passero, Sicily, Italy, at a depth of around 3500 m). The experiment consists of vertical structures, called strings, along which the optical modules are positioned. A hydrophone, located on the base of each string, is used for the reconstruction of the position of the KM3NeT elements with an accuracy of 10 cm. The presence of acoustic sensors in an underwater environment gives the opportunity to detect and study the sound emissions of marine mammals present in the area. The presented work describes the identification programs of the signals emitted by dolphins (clicks and whistles) and sperm whales (clicks) and the results of the analysis of real data collected between spring 2020 and spring 2021.

关键词： Trigger algorithms Analysis and statistical methods Trigger concepts and systems (hardware and software) data processing methods

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Machine learning classification for D⁰ meson signal extraction in d plus Au collisions

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JOURNAL OF INSTRUMENTATION 2022年第2期17卷 P02017页

作者： Bielcik, J. Hladka, K. Kramarik, L. Kus, V Czech Tech Univ Fac Nucl Sci & Phys Engn Brehova 7 Prague Czech Republic

In heavy-ion collisions at large particle colliders, such as LHC or RHIC, heavy-flavour (charm and beauty) quarks are produced mainly through initial hard scatterings. Therefore, they can serve as the probes of properties of the hot medium created in such collisions. Additionally, in small collision systems, such as d/p+Au collisions, cold nuclear matter effects can also affect the charm quark production with respect to p+p collisions. Hadrons, that contain heavy-flavour quarks, could not be directly detected, thus they are measured via reconstruction of their decay products. However, due to a large number of particles produced in such collisions, separation of the decay products from combinatorial background is challenging and advanced statistical analysis is needed. In this article, we exploit D-0(<(D-0)over bar>) -> K-pi(+)(K+pi(-)) decay in order to investigate performance of several machine learning algorithms with different implementation approaches to find the most effective way how to separate signal from random combinatorial background. For this study, we use HIJING and STAR detector simulation of d+Au collisions at root s(NN) = 200 GeV embedded to the collisions recorded with the STAR. In this paper we compare deep neural network implemented using Keras with TensorFlow backend, random forest model implemented using scikit-learn and boosted decision trees implemented by means of the Toolkit for Multivariate data Analysis with ROOT. Described methods might be applied on reconstruction of any two-body decay in high-energy physics experiments.

关键词： Analysis and statistical methods data processing methods Particle identification methods Performance of High Energy Physics Detectors

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