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CaloClouds: Fast Geometry-Independent Highly-Granular Calorimeter Simulation

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arXiv 2023年

作者： Buhmann, Erik Diefenbacher, Sascha Eren, Engin Gaede, Frank Kasicezka, Gregor Korol, Anatolii Korcari, William Krüger, Katja McKeown, Peter Institut für Experimentalphysik Universität Hamburg Luruper Chaussee 149 Hamburg22607 Germany Physics Division Lawrence Berkeley National Laboratory 1 Cyclotron Rd BerkeleyCA94720 United States Deutsches Elektronen-Synchrotron DESY Notkestr. 85 Hamburg22607 Germany Center for Data and Computing in Natural Sciences CDCS Deutsches Elektronen-Synchrotron DESY Notkestr. 85 Hamburg22607 Germany

Simulating showers of particles in highly-granular detectors is a key frontier in the application of machine learning to particle physics. Achieving high accuracy and speed with generative machine learning models would enable them to augment traditional simulations and alleviate a major computing constraint. This work achieves a major breakthrough in this task by, for the first time, directly generating a point cloud of a few thousand space points with energy depositions in the detector in 3D space without relying on a fixed-grid structure. This is made possible by two key innovations: i) Using recent improvements in generative modeling we apply a diffusion model to generate photon showers as high-cardinality point clouds. ii) These point clouds of up to 6, 000 space points are largely geometry-independent as they are down-sampled from initial even higher-resolution point clouds of up to 40, 000 so-called Geant4 steps. We showcase the performance of this approach using the specific example of simulating photon showers in the planned electromagnetic calorimeter of the International Large Detector (ILD) and achieve overall good modeling of physically relevant distributions. Copyright © 2023, The Authors. All rights reserved.

关键词： Photons

Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flows

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arXiv 2024年

作者： Buss, Thorsten Gaede, Frank Kasieczka, Gregor Krause, Claudius Shih, David Institut für Experimentalphysik Universität Hamburg Luruper Chaussee 149 Hamburg22761 Germany Deutsches Elektronen-Synchrotron DESY Notkestr. 85 Hamburg22607 Germany Center for Data and Computing in Natural Sciences CDCS Deutsches Elektronen-Synchrotron DESY Notkestr. 85 Hamburg22607 Germany Institut für theoretische Physik Universität Heidelberg Philosophenweg 12 Heidelberg69120 Germany Dominikanerbastei 16 WienA-1010 Austria NHETC Department of Physics and Astronomy Rutgers University PiscatawayNJ08854 United States

In the quest to build generative surrogate models as computationally efficient alternatives to rule-based simulations, the quality of the generated samples remains a crucial frontier. So far, normalizing flows have been among the models with the best fidelity. However, as the latent space in such models is required to have the same dimensionality as the data space, scaling up normalizing flows to high dimensional datasets is not straightforward. The prior L2LFlows approach successfully used a series of separate normalizing flows and sequence of conditioning steps to circumvent this problem. In this work, we extend L2LFlows to simulate showers with a 9-times larger profile in the lateral direction. To achieve this, we introduce convolutional layers and U-Net-type connections, move from masked autoregressive flows to coupling layers, and demonstrate the successful modelling of showers in the ILD Electromagnetic Calorimeter as well as dataset 3 from the public CaloChallenge dataset. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolution

NON-SPLITTING EULERIAN-LAGRANGIAN WENO SCHEMES FOR TWO-DIMENSIONAL NONLINEAR CONVECTION-DIFFUSION EQUATIONS

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arXiv 2024年

作者： Zheng, Nanyi Cai, Xiaofeng Qiu, Jing-Mei Qiu, Jianxian Department of Mathematical Sciences University of Delaware NewarkDE19716 United States Research Center for Mathematics Advanced Institute of Natural Sciences Beijing Normal University Zhuhai519087 China Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNU-HKBU United International College Zhuhai519087 China School of Mathematical Sciences Fujian Provincial Key Laboratory of Mathematical Modeling and High-Performance Scientific Computing Xiamen University Fujian Xiamen361005 China

In this paper, we develop high-order, conservative, non-splitting Eulerian-Lagrangian (EL) Runge-Kutta (RK) finite volume (FV) weighted essentially non-oscillatory (WENO) schemes for convection-diffusion equations. The proposed EL-RK-FV-WENO scheme defines modified characteristic lines and evolves the solution along them, significantly relaxing the time-step constraint for the convection term. The main algorithm design challenge arises from the complexity of constructing accurate and robust reconstructions on dynamically varying Lagrangian meshes. This reconstruction process is needed for flux evaluations on time-dependent upstream quadrilaterals and time integrations along moving characteristics. To address this, we propose a strategy that utilizes a WENO reconstruction on a fixed Eulerian mesh for spatial reconstruction, and updates intermediate solutions on the Eulerian background mesh for implicit-explicit RK temporal integration. This strategy leverages efficient reconstruction and remapping algorithms to manage the complexities of polynomial reconstructions on time-dependent quadrilaterals, while ensuring local mass conservation. The proposed scheme ensures mass conservation due to the flux-form semi-discretization and the mass-conservative reconstruction on both background and upstream cells. Extensive numerical tests have been performed to verify the effectiveness of the proposed scheme. © 2024, CC BY.

关键词： Mesh generation

Enhanced prairie dog optimization with Levy flight and dynamic opposition-based learning for global optimization and engineering design problems

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Neural computing and Applications 2024年第19期36卷 11137-11170页

作者： Biswas, Saptadeep Shaikh, Azharuddin Ezugwu, Absalom El-Shamir Greeff, Japie Mirjalili, Seyedali Bera, Uttam Kumar Abualigah, Laith Department of Mathematics National Institute of Technology Agartala Tripura Agartala India Unit for Data Science and Computing North-West University 11 Hoffman Street Potchefstroom2520 South Africa Faculty of Natural and Agricultural Sciences School of Computer Science and Information Systems North-West University Vanderbijlpark South Africa Centre for Artificial Intelligence Research and Optimization Torrens University Australia Fortitude Valley BrisbaneQLD4006 Australia Research Center University of Tabuk Tabuk71491 Saudi Arabia Computer Science Department Al al-Bayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia College of Engineering Yuan Ze University Taoyuan32003 Taiwan MEU Research Unit Middle East University Amman11831 Jordan

This study proposes a new prairie dog optimization algorithm version called EPDO. This new version aims to address the issues of premature convergence and slow convergence that were observed in the original PDO algorithm. To improve performance, several modifications are introduced in EPDO. First, a dynamic opposite learning strategy is employed to increase the diversity of the population and prevent premature convergence. This strategy helps the algorithm avoid falling into local optima and promotes global optimization. Additionally, the Lévy dynamic random walk technique is utilized in EPDO. This modified Lévy flight with random walk reduces the algorithm’s running time for the test function’s ideal value, accelerating its convergence. The proposed approach is evaluated using 33 benchmark problems from CEC 2017 and compared against seven other comparative techniques: GWO, MFO, ALO, WOA, DA, SCA, and RSA. Numerical results demonstrate that EPDO produces good outcomes and performs well in solving benchmark problems. To further validate the results and assess reliability, the authors employ average rank tests, the measurement of alternatives, and ranking according to the compromise solution (MARCOS) method, as well as a convergence report of EPDO and other algorithms. Furthermore, the effectiveness of the EPDO algorithm is demonstrated by applying it to five design problems. The results indicate that EPDO achieves impressive outcomes and proves its capability to address practical issues. The algorithm performs well in solving benchmark and practical design problems, as supported by the numerical results and validation methods used in the study. © The Author(s) 2024.

关键词： Global optimization

STEP: a tool to perform tests of smoothness on differential distributions based on expansion of polynomials

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arXiv 2021年

作者： Connor, Patrick L.S. Žlebčík, Radek Institut für Experimentalphysik Center for Data and Computing in natural Sciences Universität Hamburg Hamburg Germany Charles University Prague Czech Republic

We motivate and describe a method based on fits with polynomials to test the smoothness of differential distributions. As a demonstration, we apply the method to several measurements of inclusive jet double-differential cross section in the jet transverse momentum and rapidity at the Tevatron and LHC. This method opens new possibilities to test the quality of differential distributions used for the extraction of physics quantities such as the strong coupling. Copyright © 2021, The Authors. All rights reserved.

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Accurate and robust methods for direct background estimation in resonant anomaly detection

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Physical Review D 2025年第9期111卷 094041-094041页

作者： Ranit Das Thorben Finke Marie Hein Gregor Kasieczka Michael Krämer Alexander Mück David Shih NHETC Department of Physics and Astronomy Rutgers University Piscataway New Jersey 08854 USA Institute for Theoretical Particle Physics and Cosmology RWTH Aachen University D-52056 Aachen Germany Institut für Experimentalphysik Universität Hamburg 22761 Hamburg Germany Center for Data and Computing in Natural Sciences (CDCS) 22607 Hamburg Germany

Resonant anomaly detection methods have great potential for enhancing the sensitivity of traditional bump hunt searches. A key component of these methods is a high quality background template used to produce an anomaly score. Using the LHC Olympics R&D dataset, we demonstrate that this background template can also be repurposed to directly estimate the background expectation in a simple cut and count setup. In contrast to a traditional bump hunt, no fit to the invariant mass distribution is needed, thereby avoiding the potential problem of background sculpting. Furthermore, direct background estimation allows working with large background rejection rates, where resonant anomaly detection methods typically show their greatest improvement in significance.

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Effect of copper nanoparticle volume fraction on flow in a 3D lid-driven cavity with phase change materials using molecular dynamics simulation

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Case Studies in Chemical and Environmental Engineering 2025年 11卷

作者： Sawaran Singh, Narinderjit Singh Hassan, Waqed H. Thiab, Rifqa F. Al-zahy, Younis Mohamed Atiah Salahshour, Soheil Hekmatifar, M. Faculty of Data Science and Information Technology INTI International University Persiaran Perdana BBN Putra Nilai Nilai 71800 Malaysia University of Warith Al-Anbiyaa Kerbala 56001 Iraq Department of Civil Engineering College of Engineering University of Kerbala Kerbala 56001 Iraq College of Health and Medical Techniques Al-Zahraa University for Women Karbala 56100 Iraq Department of Physics College of Education Misan University Maysan Iraq Faculty of Engineering and Natural Sciences Istanbul Okan University Istanbul Turkey Faculty of Engineering and Natural Sciences Bahcesehir University Istanbul Turkey Research Center of Applied Mathematics Khazar University Baku Azerbaijan Fast Computing Center Shabihsazan Ati Pars Tehran Iran

Background: Phase Change Materials are substances characterized by specific properties, including defined melting points and substantial latent heat of fusion. Effective heat transfer management is vital in modern industries, as it supports essential processes across various sectors. Methods: This study investigates the effect of copper nanoparticle volume fraction on flow behavior and thermal dynamics in a 3D lid-driven cavity. This enclosure was a controlled environment filled with phase change materials, designed to optimize thermal energy management. The system configuration comprised a wavy bottom wall and an adaptable upper wall, allowing for dynamic adjustments during the simulation. The results show that as the volume ratio of copper nanoparticles increased from 1 % to 3 %, the steady heat transfer process in the simulated nanoparticles also increased. Increasing the volume ratio from 1 % to 3 % resulted in a decrease in the maximum density of nanoparticles, which decreased from 0.0152 to 0.0146 atom/Å³. Additionally, this increase led to a rise in thermal conductivity from 1.26 to 1.45 W/m·K and in heat flux from 8.26 to 9.95 W/m². Significant Findings: The study demonstrates that optimizing the volume fraction of Cu-NPs in PCMs can significantly enhance thermal conductivity and heat flux, offering potential improvements in thermal energy storage systems. © 2025 The Authors

关键词： Density Heat flux Molecular dynamics simulation Phase change material Thermal conductivity

Carbon-doped percentage effect on the mechanical properties of nanoporous silicon sample using molecular dynamics simulation

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Case Studies in Chemical and Environmental Engineering 2025年 11卷

作者： Sawaran Singh, Narinderjit Singh Ali, Ali B.M. Ameen, Hawzhen Fateh M. Al-Zahiwat, Murtadha M. Salahshour, Soheil Emami, Nafiseh Faculty of Data Science and Information Technology INTI International University Persiaran Perdana BBN Putra Nilai Nilai 71800 Malaysia Air Conditioning Engineering Department College of Engineering University of Warith Al-Anbiyaa Karbala Iraq Department of Petroleum Technology Erbil Technology College Erbil Polytechnic University Kurdistan Region Erbil Iraq Department of Petroleum Engineering Knowledge University Erbil Iraq Department of Chemical engineering College of Engineering University of Misan Amarah Iraq Faculty of Engineering and Natural Sciences Istanbul Okan University Istanbul Türkiye Faculty of Engineering and Natural Sciences Bahcesehir University Istanbul Türkiye Research Center of Applied Mathematics Khazar University Baku Türkiye Fast Computing Center Shabihsazan Ati Pars Tehran Iran

Porous materials have attracted considerable attention from researchers due to its many uses in molecular separation, heterogeneous catalysis, absorption technologies, and electronic improvements. These solid materials, often defined by their structural voids, are essential in several sectors. This research investigated the impact of carbon doping on the mechanical characteristics of nanoporous silicon matrices. The use of high-purity silicon doping is very beneficial in the semiconductor industry and is crucial for high-power devices and automotive applications. This study simulates a nanoporous silicon sample by molecular dynamics methods, adding carbon doping at different concentrations. The findings demonstrate that when the carbon doping concentration escalated from 1 % to 30 %, the mechanical resistance of the system decreased correspondingly. The ultimate tensile strength fell from 10.26 to 9.02 GPa. Furthermore, Young's modulus rose from 83.47 to 98.37 GPa. The decline in mechanical stability was associated with a drop in the model's total weight, which had considerable ramifications for industrial applications. Thus, incorporating C-doped nanoporous silicon into real applications not only lowered the weight of target materials but also improved their use. © 2025 The Author(s)

关键词： Carbon doping Mechanical properties Molecular dynamics simulation Nanoporous Porous materials

PlantMWpIDB: a database for the molecular weight and isoelectric points of the plant proteomes

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Scientific Reports 2022年第1期12卷 1-7页

作者： Mohanta, Tapan Kumar Kamran, Muhammad Shahzad Omar, Muhammad Anwar, Waheed Choi, Gyu Sang Natural and Medical Sciences Research Center University of Nizwa Nizwa 616 Oman Department of Computer Science and IT The Islamia University of Bahawalpur Bahawalpur Pakistan Department of Data Science Faculty of Computing The Islamia University of Bahawalpur Bahawalpur Pakistan Department of Information and Communication Engineering Yeungnam University 214-1 Gyeongsan-si 712-749 South Korea

The molecular weight and isoelectric point of the proteins are very important parameters that control their subcellular localization and subsequent function. Although the genome sequence data of the plant kingdom improved enormously, the proteomic details have been poorly elaborated. Therefore, we have calculated the molecular weight and isoelectric point of the plant proteins and reported them in this database. A database, PlantMWpIDB, containing protein data from 342 plant proteomes was created to provide information on plant proteomes for hypothesis formulation in basic research and for biotechnological applications. The Molecular weight and isoelectric point (pI) are important molecular parameters of proteins that are useful when conducting protein studies involving 2D gel electrophoresis, liquid chromatography-mass spectrometry, and X-ray protein crystallography. PlantMWpIDB provides an easy-to-use and efficient interface for search options and generates a summary of basic protein parameters. The database represents a virtual 2D proteome map of plants, and the molecular weight and pI of a protein can be obtained by searching on the name of a protein, a keyword, or by a list of accession numbers. The PlantMWpIDB database also allows one to query protein sequences. The database can be found in the following link https://***/. The individual 2D virtual proteome map of the plant kingdom will enable us to understand the proteome diversity between different species. Further, the molecular weight and isoelectric point of individual proteins can enable us to understand their functional significance in different species. © 2022, The Author(s).

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