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Correction to:Production and test of sPHENIX W/SciFiber electromagnetic calorimeter blocks in China

nuclear science and Techniques 2024年第11期35卷 138-138页

作者： Xiao-Zhou Yu Xi-Yang Wang Wei-Hu Ma Shi-Hong Fu Peng-Fei Sun Jin-Xing Song Wan-Bing He Yang Shen Long Ma Jin-Hui Chen Huan-Zhong Huang Si-Guang Wang Jing Zhou Xiao-Mei Li Institute of Modern Physics Fudan UniversityShanghai 200433China Key Laboratory of Nuclear Physics and Ion‑Beam Application(MOE) Fudan UniversityShanghai 200433China Science and Technology on Nuclear Data Laboratory China Institute of Atomic EnergyBeijing 102413China Department of Physics and Astronomy University of CaliforniaLos AngelesCA 90095USA State Key Laboratory of Nuclear Physics and Technology School of PhysicsPeking UniversityBeijing 100871China

Correction to:nuclear science and Techniques(2024)35:145 https://***/10.1007/s41365-024-01517-y In this article the author’s name Wan-Bing He was incorrectly written as Wan-Bin *** original article has been corrected.

关键词： blocks corrected calorimeter

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Dense matter with eXTP

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science China(Physics,Mechanics & Astronomy) 2019年第2期62卷 28-44页

作者： Anna L.Watts WenFei Yu Juri Poutanen Shu Zhang Sudip Bhattacharyya Slavko Bogdanov Long Ji Alessandro Patruno Thomas E.Riley Pavel Bakala Altan Baykal Federico Bernardini Ignazio Bombaci Edward Brown Yuri Cavecchi Deepto Chakrabarty Jér?me Chenevez Nathalie Degenaar Melania Del Santo Tiziana Di Salvo Victor Doroshenko Maurizio Falanga Robert D.Ferdman Marco Feroci Angelo F.Gambino MingYu Ge Svenja K.Greif Sebastien Guillot Can Gungor Dieter H.Hartmann Kai Hebeler Alexander Heger Jeroen Homan Rosario Iaria Jean in 't Zand Oleg Kargaltsev Aleksi KurkelaTheoretical Physics Department CERN XiaoYu Lai Ang Li XiangDong Li ZhaoSheng Li Manuel Linares FangJun Lu Simin Mahmoodifar Mariano Méndez M.Coleman Miller Sharon Morsink Joonas N?ttil? Andrea Possenti Chanda Prescod-Weinstein JinLu Qu Alessandro Riggio Tuomo Salmi Andrea Sanna Andrea Santangelo Hendrik Schatz Achim Schwenk LiMing Song Eva?rámková Benjamin Stappers Holger Stiele Tod Strohmayer Ingo Tews Laura Tolos Gabriel T?r?k David Tsang Martin Urbanec Andrea Vacchi RenXin Xu YuPeng Xu Silvia Zane GuoBao Zhang ShuangNan Zhang WenDa Zhang ShiJie Zheng Xia Zhou Anton Pannekoek Institute for Astronomy University of Amsterdam Shanghai Astronomical Observatory Tuorla Observatory Department of Physics and Astronomy University of Turku Nordita KTH Royal Institute of Technology and Stockholm University Institute of High Energy Physics Chinese Academy Sciences Tata Institute of Fundamental Research Columbia Astrophysics Laboratory Columbia University Institut für Astronomie und Astrophysik Tübingen Universit?t Tübingen Leiden Observatory Leiden University Research Center for Computational Physics and Data Processing Silesian University in Opava Physics Department Middle East Technical University INAF Osservatorio Astronomico di Roma New York University Abu Dhabi Dipartimento di Fisica Enrico Fermi University of Pisa INFN Italian National Institute for Nuclear Physics Department of Physics and Astronomy Michigan State University Department of Astrophysical Sciences Princeton University Mathematical Sciences and STAG Research Centre University of Southampton MIT Kavli Institute for Astrophysics and Space Research DTU Space Technical University of Denmark INAF/IASF Palermo via Ugo La Malfa 153 Universita degli Studi di Palermo Dipartimento di Fisica e Chimica International Space Science Institute (ISSI) Faculty of Science University of East Anglia INAF Istituto di Astrofisica e Planetologie Spaziali Institut für Kernphysik Technische Universit?t Darmstadt ExtreMe Matter Institute EMMI GSI Helmholtzzentrum für Schwerionenforschung GmbH Instituto de Astrof'?sica Pontificia Universidad Católica de Chile Department of Physics & Astronomy Kinard Lab of Physics Clemson University School of Physics and Astronomy Monash University SRON Netherlands Institute for Space Research Department of Physics The George Washington University Faculty of Science and Technology University of Stavanger School of Physics and Mechanical & Electrical Engineering Hubei University of Education Department of Astronomy Xiamen University (Haiyun Campus) School of Astronomy and Space

In this White Paper we present the potential of the Enhanced X-ray Timing and Polarimetry(eXTP) mission for determining the nature of dense matter; neutron star cores host an extreme density regime which cannot be replicated in a terrestrial laboratory. The tightest statistical constraints on the dense matter equation of state will come from pulse profile modelling of accretion-powered pulsars, burst oscillation sources, and rotation-powered pulsars. Additional constraints will derive from spin measurements, burst spectra, and properties of the accretion flows in the vicinity of the neutron star. Under development by an international Consortium led by the Institute of High Energy Physics of the Chinese Academy of sciences, the eXTP mission is expected to be launched in the mid 2020 s.

关键词： neutron X-rays dense matter equation of state

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‘Flux+Mutability’: a conditional generative approach to one-class classification and anomaly detection

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Machine Learning: science and technology 2022年第4期3卷 045012页

作者： Fanelli, C. Giroux, J. Papandreou, Z. Laboratory for Nuclear Science Massachusetts Institute of Technology CambridgeMA02139 United States The NSF AI Institute for Artificial Intelligence and Fundamental Interactions WilliamsburgVA23185 United States William & Mary Data Science WilliamsburgVA23185 United States Jefferson Lab Newport NewsVA23606 United States Department of Physics University of Regina ReginaSKS4S 0A2 Canada

Anomaly Detection is becoming increasingly popular within the experimental physics community. At experiments such as the Large Hadron Collider, anomaly detection is growing in interest for finding new physics beyond the Standard Model. This paper details the implementation of a novel Machine Learning architecture, called Flux+Mutability, which combines cutting-edge conditional generative models with clustering algorithms. In the ‘flux’ stage we learn the distribution of a reference class. The ‘mutability’ stage at inference addresses if data significantly deviates from the reference class. We demonstrate the validity of our approach and its connection to multiple problems spanning from one-class classification to anomaly detection. In particular, we apply our method to the isolation of neutral showers in an electromagnetic calorimeter and show its performance in detecting anomalous dijets events from standard QCD background. This approach limits assumptions on the reference sample and remains agnostic to the complementary class of objects of a given problem. We describe the possibility of dynamically generating a reference population and defining selection criteria via quantile cuts. Remarkably this flexible architecture can be deployed for a wide range of problems, and applications like multi-class classification or data quality control are left for further exploration. © 2022 The Author(s). Published by IOP Publishing Ltd.

关键词： Anomaly detection

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Polynomial-SHAP analysis of liver disease markers for capturing of complex feature interactions in machine learning models

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Computers in Biology and Medicine 2024年 182卷 109168-109168页

作者： Ejiyi, Chukwuebuka Joseph Cai, Dongsheng Ejiyi, Makuachukwu B. Chikwendu, Ijeoma A. Coker, Kenneth Oluwasanmi, Ariyo Bamisile, Oluwatoyosi F. Ejiyi, Thomas U. Qin, Zhen College of Nuclear Technology and Automation Engineering Sichuan Industrial Internet Intelligent Monitoring and Application Engineering Research Center Chengdu University of Technology Sichuan Chengdu China Pharmacy Department University of Nigeria Nsukka Enugu State Nsukka Nigeria School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China Department of Electrical and Electronic Engineering Ho Technical University Ghana Ghana Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Sichuan Chengdu China Department of Pure and Industrial Chemistry University of Nigeria Nsukka Enugu State Nsukka Nigeria

Liver disease diagnosis is pivotal for effective patient management, and machine learning techniques have shown promise in this domain. In this study, we investigate the impact of Polynomial-SHapley Additive exPlanations analysis on enhancing the performance and interpretability of machine learning models for liver disease classification. Our results demonstrate significant improvements in accuracy, precision, recall, F1_score, and Matthews correlation coefficient across various algorithms when polynomial- SHapley Additive exPlanations analysis is applied. Specifically, the Light Gradient Boosting Machine model achieves exceptional performance with 100 % accuracy in both scenarios. Furthermore, by comparing the results obtained with and without the approach, we observe substantial differences in the performance, highlighting the importance of incorporating Polynomial-SHapley Additive exPlanations analysis for improved model performance. The Polynomial features and SHapley Additive exPlanations values also enhance the interpretability of machine learning models by capturing complex feature interactions, enabling users to gain deeper insights into the underlying mechanisms driving the diagnosis. Moreover, data rebalancing using Synthetic Minority Over-sampling Technique and parameter tuning were employed to optimize the performance of the models. These findings underscore the significance of employing this analytical approach in machine-learning-based diagnostic systems for liver diseases, offering superior performance and enhanced interpretability for informed decision-making in clinical practice. © 2024 Elsevier Ltd

关键词： Adversarial machine learning

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An Efficient Digital Twin Based on Machine Learning Svd Autoencoder and Generalised Latent Assimilation for nuclear Reactor Physics

SSRN

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SSRN 2022年

作者： Gong, Helin Cheng, Sibo Chen, Zhang Li, Qing Quilodrán-Casas, César Xiao, Dunhui Arcucci, Rossella SJTU-ParisTech Elite Institute of Technology Shanghai 200240 China Data Science Institute Imperial College London LondonSW7 2AZ United Kingdom Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu610041 China Faculty of Sciences and Engineering Swansea University SwanseaSA1 8EN United Kingdom Department of Earth Science & Engineering Imperial College London LondonSW7 2AZ United Kingdom

This paper proposes an approach that combines reduced-order models with machine learning in order to create an digital twin to predict the power distribution over the core during the operation stage. The operational digital twin is designed to solve forward problems given input operation parameters, as well as to solve inverse problems given some observations of the power field. The forward model is realised using SVD autoencoder reduced order model and machine learning methods. The inverse model is based on a generalised latent assimilation method. The effectiveness in the sense of accuracy and real-time solver of the digital twin is illustrated through a real engineering problem in nuclear reactor physics - reactor core simulation in the life cycle of HPR1000 affected by control rod inserting step, burnup, power level and inlet temperature of the coolant, which shows potential applications for on-line monitoring purpose. © 2022, The Authors. All rights reserved.

关键词： Inverse problems

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Observation of Reverse Saturable Absorption of an X-ray Laser

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Physical Review Letters 2017年第7期119卷 075002-075002页

作者： B. I. Cho M. S. Cho M. Kim H.-K. Chung B. Barbrel K. Engelhorn T. Burian J. Chalupský O. Ciricosta G. L. Dakovski V. Hájková M. Holmes L. Juha J. Krzywinski R. W. Lee Chang Hee Nam D. S. Rackstraw S. Toleikis J. J. Turner S. M. Vinko J. S. Wark U. Zastrau P. A. Heimann Center for Relativistic Laser Science Institute for Basic Science (IBS) Gwangju 61005 Korea Department of Physics and Photon Science Gwangju Institute of Science and Technology Gwangju 61005 Korea Atomic and Molecular Data Unit Nuclear Data Section IAEA P.O. Box 100 A-1400 Vienna Austria Lawrence Berkeley National Laboratory 1 Cyclotron Road Berkeley California 94720 USA Institute of Physics ASCR Na Slovance 2 18221 Prague 8 Czech Republic Department of Physics Clarendon Laboratory University of Oxford Parks Road Oxford OX1 3PU United Kingdom SLAC National Accelerator Laboratory 2575 Sand Hill Road Menlo Park California 94025 USA Department of Physics University of California Berkeley California 94720 USA Deutsches-Elektronensynchrotron DESY Notkestrasse 85 D-22603 Hamburg Germany European XFEL Holzkoppel 4 22869 Schenefeld Germany

A nonlinear absorber in which the excited state absorption is larger than the ground state can undergo a process called reverse saturable absorption. It is a well-known phenomenon in laser physics in the optical regime, but is more difficult to generate in the x-ray regime, where fast nonradiative core electron transitions typically dominate the population kinetics during light matter interactions. Here, we report the first observation of decreasing x-ray transmission in a solid target pumped by intense x-ray free electron laser pulses. The measurement has been made below the K-absorption edge of aluminum, and the x-ray intensity ranges are 1016 –1017 W/cm2. It has been confirmed by collisional radiative population kinetic calculations, underscoring the fast spectral modulation of the x-ray pulses and charge states relevant to the absorption and transmission of x-ray photons. The processes shown through detailed simulations are consistent with reverse saturable absorption, which would be the first observation of this phenomena in the x-ray regime. These light matter interactions provide a unique opportunity to investigate optical transport properties in the extreme state of matters, as well as affording the potential to regulate ultrafast x-ray free-electron laser pulses.

关键词： High-energy-density plasmas Laser light absorption in plasmas Multiphoton or tunneling ionization & excitation Plasma production & heating by laser beams, laser-foil, laser-cluster X-ray beams & optics Hot dense plasma

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Physics-informed Neural Network for system identification of rotors ⁎

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IFAC-PapersOnLine 2024年第15期58卷 307-312页

作者： Xue Liu Wei Cheng Ji Xing Xuefeng Chen Zhibin Zhao Rongyong Zhang Qian Huang Jinqi Lu Hongpeng Zhou Wei Xing Zheng Wei Pan The National Key Laboratory of Aerospace Power System and Plasma Technology and School of Mechanical Engineering Xi’an Jiaotong University Xi’an China China Nuclear Power Engineering Co. Ltd. Beijing China Shanghai Apollo Machinery Co. Ltd. Shanghai China Department of Computer Science University of Manchester Manchester United Kingdom School of Computer Data and Mathematical Sciences Western Sydney University Sydney Australia

The condition of the rotor system remains difficult to assess due to system nonlinearity and nosiy measurements. To deal with the problem, we proposed a hierarchical physics-informed neural network (HPINN) to discover the ordinary differential equation (ODE) of a healthy/faulty rotor system from noise measurements and then assess the machine condition based on the discovered ODE. Specifically, the ODE of a healthy rotor system is first stably identified from noise measurement through HPINN guided by rotor dynamics. Based on the identified healthy ODE, the extra fault terms in the ODE of the faulty rotor system are then sparsely regressed from the predefined library embedded in HPINN. Moreover, with the mathematical terms of discovered fault, the potential fault and the health indicator (HI) are diagnosed and constructed to assess the condition of the rotor system, respectively. Finally, the effectiveness of the proposed method is verified by the data set collected on the circulating water test bench, showing the potential for practical applications.

关键词： rotor system fault diagnosis health indicator physics-informed neural network

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Estimating geographic variation of infection fatality ratios during epidemics

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Infectious Disease Modelling 2024年第2期9卷 634-643页

作者： Joshua Ladau Eoin L.Brodie Nicola Falco Ishan Bansal Elijah B.Hoffman Marcin P.Joachimiak Ana M.Mora Angelica M.Walker Haruko M.Wainwright Yulun Wu Mirko Pavicic Daniel Jacobson Matthias Hess James B.Brown Katrina Abuabara Departments of Computational Precision Health and Dermatology University of CaliforniaSan FranciscoCA94115USA Arva Intelligence Inc.Salt Lake CityUT84101USA Computational Biosciences Group Lawrence Berkeley National LaboratoryBerkeleyCA94720USA Earth and Environmental Sciences Area Lawrence Berkeley National LaboratoryBerkeleyCA94720USA Graduate Group in Biostatistics University of CaliforniaBerkeleyCA94720USA Biosystems Data Science Environmental Genomics and Systems BiologyLawrence Berkeley National LaboratoryBerkeleyCA94720USA Center for Environmental Research and Community Health(CERCH) School of Public HealthUniversity of CaliforniaBerkeleyCA94720USA Bredesen Center for Interdisciplinary Research and Graduate Education University of TennesseeKnoxvilleTN37996USA Department of Nuclear Science and Engineering Massachusetts Institute of TechnologyBostonMA02139USA Biosciences Oak Ridge National LaboratoryOak RidgeTN37830USA University of California DavisCA95616USA Statistics Department University of CaliforniaBerkeleyCA94720USA Division of Epidemiology and Biostatistics University of California Berkeley School of Public Health2121 Berkeley WayBerkeleyCA94720USA

Objectives We aim to estimate geographic variability in total numbers of infections and infection fatality ratios(IFR;the number of deaths caused by an infection per 1,000 infected people)when the availability and quality of data on disease burden are limited during an *** We develop a noncentral hypergeometric framework that accounts for differential probabilities of positive tests and reflects the fact that symptomatic people are more likely to seek *** demonstrate the robustness,accuracy,and precision of this framework,and apply it to the United States(U.S.)COVID-19 pandemic to estimate county-level SARS-CoV-2 *** The estimators for the numbers of infections and IFRs showed high accuracy and precision;for instance,when applied to simulated validation data sets,across counties,Pearson correlation coefficients between estimator means and true values were 0.996 and 0.928,respectively,and they showed strong robustness to model *** the county-level estimators to the real,unsimulated COVID-19 data spanning April 1,2020 to September 30,2020 from across the U.S.,we found that IFRs varied from 0 to 44.69,with a standard deviation of 3.55 and a median of *** The proposed estimation framework can be used to identify geographic variation in IFRs across settings.

关键词： Infection fatality ratio Infection fatality rate Noncentral hypergeometric distribution COVID-19 SARS-CoV-2

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Light atom quantum oscillations in UC and US

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Physical Review B 2016年第1期93卷 014306-014306页

作者： Yuen Yiu A. A. Aczel G. E. Granroth D. L. Abernathy M. B. Stone W. J. L. Buyers J. Y. Y. Lin G. D. Samolyuk G. M. Stocks S. E. Nagler Department of Physics and Astronomy University of Tennessee Knoxville Tennessee 37996 USA Quantum Condensed Matter Division Oak Ridge National Laboratory Oak Ridge Tennessee 37831 USA Neutron Data Analysis and Visualization Division Oak Ridge National Laboratory Oak Ridge Tennessee 37831 USA Canadian Neutron Beam Centre Canadian Nuclear Laboratories Chalk River Ontario Canada K0J 1J0 California Institute of Technology Pasadena California 91125 USA Materials Science and Technology Division Oak Ridge National Laboratory Oak Ridge Tennessee 37831 USA

High-energy vibrational scattering in the binary systems UC and US is measured using time-of-flight inelastic neutron scattering. A clear set of well-defined peaks equally separated in energy is observed in UC, corresponding to harmonic oscillations of the light C atoms in a cage of heavy U atoms. The scattering is much weaker in US and only a few oscillator peaks are visible. We show how the difference between the materials can be understood by considering the neutron scattering lengths and masses of the lighter atoms. Monte Carlo ray tracing is used to simulate the scattering, with near quantitative agreement with the data in UC, and some differences with US. The possibility of observing anharmonicity and anisotropy in the potentials of the light atoms is investigated in UC. Overall, the observed data is well accounted for by considering each light atom as a single atom isotropic quantum harmonic oscillator.

关键词： Phonons Inelastic neutron scattering Monte Carlo methods

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Corrigendum to “Measurement of the 134 Xe(n, 2n) 133m,g Xe and 136 Xe(n, 2n) 135m,g Xe reaction cross-sections and isomeric ratios induced by the 14.8 MeV neutrons” [Appl. Radiat. Isot. 200 (2023) 110948]

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Applied Radiation and Isotopes 2023年 201卷

作者： Hao Guo Qian Li Chunlei Zhang Liyang Jiang Xichao Ruan Qi Sun Chao Liu Key Laboratory of Nuclear Data China Institute of Atomic Energy BeiJing 102413 China College of Nuclear Science and Technology Beijing Normal University BeiJing 100875 China

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