Computer Physics Communications最新文献

{"title":"One-to-one correspondence reconstruction at the electron-positron Higgs factory","authors":"Yuexin Wang ,&nbsp;Hao Liang ,&nbsp;Yongfeng Zhu ,&nbsp;Yuzhi Che ,&nbsp;Xin Xia ,&nbsp;Huilin Qu ,&nbsp;Chen Zhou ,&nbsp;Xuai Zhuang ,&nbsp;Manqi Ruan","doi":"10.1016/j.cpc.2025.109661","DOIUrl":"10.1016/j.cpc.2025.109661","url":null,"abstract":"<div><div>We propose one-to-one correspondence reconstruction for electron-positron Higgs factories. For each visible particle, one-to-one correspondence aims to associate relevant detector hits with only one reconstructed particle and accurately identify its species. To achieve this goal, we develop a novel detector concept featuring 5-dimensional calorimetry that provides spatial, energy, and time measurements for each hit, and a reconstruction framework that combines state-of-the-art particle flow and machine learning algorithms. In the benchmark process of Higgs to di-jets, over 91% of visible energy can be successfully mapped into well-reconstructed particles that not only maintain a one-to-one correspondence relationship but also associate with the correct combination of cluster and track, improving the invariant mass resolution of hadronically decayed Higgs bosons by 25%. Performing simultaneous identification on these well-reconstructed particles, we observe efficiencies of 97% to nearly 100% for charged particles (<span><math><msup><mrow><mi>e</mi></mrow><mrow><mo>±</mo></mrow></msup></math></span>, <span><math><msup><mrow><mi>μ</mi></mrow><mrow><mo>±</mo></mrow></msup></math></span>, <span><math><msup><mrow><mi>π</mi></mrow><mrow><mo>±</mo></mrow></msup></math></span>, <span><math><msup><mrow><mi>K</mi></mrow><mrow><mo>±</mo></mrow></msup></math></span>, <span><math><mi>p</mi><mo>/</mo><mover><mrow><mi>p</mi></mrow><mrow><mo>¯</mo></mrow></mover></math></span>) and photons (<em>γ</em>), and 75% to 80% for neutral hadrons (<span><math><msubsup><mrow><mi>K</mi></mrow><mrow><mi>L</mi></mrow><mrow><mn>0</mn></mrow></msubsup></math></span>, <em>n</em>, <span><math><mover><mrow><mi>n</mi></mrow><mrow><mo>¯</mo></mrow></mover></math></span>). For physics measurements of Higgs to invisible and exotic decays, golden channels to probe new physics, one-to-one correspondence could enhance discovery power by 10% to up to a factor of two. This study demonstrates the necessity and feasibility of one-to-one correspondence reconstruction at electron-positron Higgs factories.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"314 ","pages":"Article 109661"},"PeriodicalIF":7.2,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physics-informed neural networks with trainable sinusoidal activation functions for approximating the solutions of the Navier-Stokes equations","authors":"Amirhossein Khademi,&nbsp;Steven Dufour","doi":"10.1016/j.cpc.2025.109672","DOIUrl":"10.1016/j.cpc.2025.109672","url":null,"abstract":"<div><div>We present TSA-PINN, a novel Physics-Informed Neural Network (PINN) that leverages a Trainable Sinusoidal Activation (TSA) mechanism to approximate solutions to the Navier-Stokes equations. By incorporating neuron-wise sinusoidal activation functions with trainable frequencies and a dynamic slope recovery mechanism, TSA-PINN achieves superior accuracy and convergence. Its ability to dynamically adjust activation frequencies enables efficient modeling of complex fluid behaviors, reducing training time and computational cost. Our testing goes beyond canonical problems, to study less-explored and more challenging scenarios, which have typically posed difficulties for prior models. Various numerical tests underscore the efficacy of the TSA-PINN model across five different scenarios. These include steady-state two-dimensional flows in a lid-driven cavity at two different Reynolds numbers; a cylinder wake problem characterized by oscillatory fluid behavior; and two time-dependent three-dimensional turbulent flow cases. In the turbulent cases, the focus is on detailed near-wall phenomena—including the viscous sub-layer, buffer layer, and log-law region—as well as the complex interactions among eddies of various scales. Both numerical and quantitative analyses demonstrate that TSA-PINN offers substantial improvements over conventional PINN models. This research advances physics-informed machine learning, setting a new benchmark for modeling dynamic systems in scientific computing and engineering.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"314 ","pages":"Article 109672"},"PeriodicalIF":7.2,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A dynamical high-pass filter for magnetic fluctuations in full-f field-aligned turbulence codes","authors":"Kaiyu Zhang,&nbsp;Wladimir Zholobenko,&nbsp;Andreas Stegmeir,&nbsp;Konrad Eder,&nbsp;Frank Jenko","doi":"10.1016/j.cpc.2025.109670","DOIUrl":"10.1016/j.cpc.2025.109670","url":null,"abstract":"<div><div>Plasma turbulence in the edge of magnetic confinement devices is customarily treated as full-<em>f</em> due to large fluctuations. For computational efficiency, field-aligned coordinates are employed, separating the magnetic field into equilibrium <span><math><msub><mrow><mi>B</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span> and delta-<em>f</em> perturbations which are handled by the magnetic flutter operators. Evolving the full-<em>f</em> pressure with delta-<em>f</em> magnetic perturbations can cause inconsistency since the latter contain background components such as the Shafranov shift, which are actually parts of the equilibrium magnetic field. Such background components (<span><math><msub><mrow><mi>B</mi></mrow><mrow><mi>s</mi></mrow></msub></math></span>) contained in the magnetic perturbations undermine the field-aligned numerics when treated as flutter: errors arise if <span><math><msub><mrow><mi>B</mi></mrow><mrow><mi>s</mi></mrow></msub><mo>/</mo><msub><mrow><mi>B</mi></mrow><mrow><mn>0</mn></mrow></msub><mo>≪</mo><msub><mrow><mi>l</mi></mrow><mrow><mo>⊥</mo></mrow></msub><mo>/</mo><msub><mrow><mi>h</mi></mrow><mrow><mo>∥</mo></mrow></msub></math></span> is not satisfied, with the perpendicular turbulence scale <span><math><msub><mrow><mi>l</mi></mrow><mrow><mo>⊥</mo></mrow></msub></math></span> and the parallel grid distance <span><math><msub><mrow><mi>h</mi></mrow><mrow><mo>∥</mo></mrow></msub></math></span>. We find that the commonly used removal of <span><math><msub><mrow><mi>B</mi></mrow><mrow><mi>s</mi></mrow></msub></math></span> by subtracting the toroidal average of magnetic perturbations intervenes in the Alfvén dynamics, causing spurious <span><math><mi>E</mi><mo>×</mo><mi>B</mi></math></span> transport. Instead, we propose an improved method to dynamically filter out the evolving background from the turbulent magnetic fluctuations in the time domain, which is then applicable also for stellarators. The filter is verified in both low and high confinement tokamak conditions, confirming its capability to preserve the turbulence fidelity (provided sufficient filter width).</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"314 ","pages":"Article 109670"},"PeriodicalIF":7.2,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DLScanner: A parameter space scanner package assisted by deep learning methods","authors":"A. Hammad ,&nbsp;Raymundo Ramos","doi":"10.1016/j.cpc.2025.109659","DOIUrl":"10.1016/j.cpc.2025.109659","url":null,"abstract":"<div><div>In this paper, we introduce a scanner package enhanced by deep learning (DL) techniques. The proposed package addresses two significant challenges associated with previously developed DL-based methods: slow convergence in high-dimensional scans and the limited generalization of the DL network when mapping random points to the target space. To tackle the first issue, we use a similarity learning network that maps sampled points into a representation space. In this space, in-target points are grouped together while out-target points are effectively pushed apart. This approach enhances the scan convergence by refining the representation of sampled points. The second challenge is mitigated by integrating a dynamic sampling strategy. Specifically, we employ a VEGAS mapping to adaptively suggest new points for the DL network while also improving the mapping when more points are collected. Our proposed framework demonstrates substantial gains in performance and efficiency compared to other scanning methods.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"314 ","pages":"Article 109659"},"PeriodicalIF":7.2,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143946995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dielectric functions, their properties and their relation to observables: Investigations using the Chapidif program for the case of aluminum","authors":"Maarten Vos ,&nbsp;Pedro L. Grande","doi":"10.1016/j.cpc.2025.109657","DOIUrl":"10.1016/j.cpc.2025.109657","url":null,"abstract":"<div><div>We introduce the program ‘Chapidif’ by describing a study of the properties of aluminum based on simple model dielectric functions. These are generally not available from first principle, and one is forced to describe them in terms of (a sum of) model dielectric functions. The Chapidif program is used to visualize these, check their sum rules and the mathematical relation between the real and imaginary part. In addition, several properties related to the interaction of charged particles (here either protons or electrons) with matter are derived and compared with experiment. By having a single program that can calculate a range of properties, it becomes easy to ensure that the model used is not just able to describe a single observable, but it is transferable, i.e. describes reasonably well a larger range of material properties. A reflection electron energy loss measurement is used as an example of how a comparison of calculated results with experiment can be used to improve the model and thus enhance the quality of the properties derived from the dielectric function.</div></div><div><h3>Program summary</h3><div><em>Program Title:</em> Chapidif</div><div><em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/7wmxg69v7x.1</span><svg><path></path></svg></span></div><div><em>Licensing provisions:</em> CC BY NC 3.0</div><div><em>Programming language:</em> Python, C++</div><div><em>Nature of problem:</em> Frequency- and momentum-dependent dielectric functions can describe a wide variety of material properties. The quantity has many intricate mathematical properties and is subject to constraints due to sum rules. The Chapidif program can be used to visualize a dielectric function, check its sum rules, and calculate a wide range of quantities, in particular relating to the interaction of protons and electrons with matter. Details of how the classical and quantum-based dielectric functions are implemented are given elsewhere [1]. The program makes it easy to investigate if the assumed dielectric function has the required mathematical properties and how the choice of the model dielectric function and the corresponding parameters influences the calculated observables such as ion stopping and electron inelastic mean free path.</div><div><em>Solution method:</em> The program consists of a Python/Tkinter user interface and C++ backend that does the actual calculations. Results are displayed using Matplotlib library and, if desired, text-based output files containing the input parameters used and the calculated quantities can be generated.</div></div><div><h3>References</h3><div><ul><li><span>[1]</span><span><div>M. Vos, P.L. Grande, RPA dielectric functions: streamlined approach to relaxation effects, binding and high momentum dispersion, J. Phys. Chem. Solids 198 (2025) 112470, <span><span>https://doi.org/10.1016/j.jpcs.2024.112470</span><svg><path></path></svg></span>.</div></span></li></ul></div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"314 ","pages":"Article 109657"},"PeriodicalIF":7.2,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Materials Learning Algorithms (MALA): Scalable machine learning for electronic structure calculations in large-scale atomistic simulations","authors":"Attila Cangi ,&nbsp;Lenz Fiedler ,&nbsp;Bartosz Brzoza ,&nbsp;Karan Shah ,&nbsp;Timothy J. Callow ,&nbsp;Daniel Kotik ,&nbsp;Steve Schmerler ,&nbsp;Matthew C. Barry ,&nbsp;James M. Goff ,&nbsp;Andrew Rohskopf ,&nbsp;Dayton J. Vogel ,&nbsp;Normand Modine ,&nbsp;Aidan P. Thompson ,&nbsp;Sivasankaran Rajamanickam","doi":"10.1016/j.cpc.2025.109654","DOIUrl":"10.1016/j.cpc.2025.109654","url":null,"abstract":"<div><div>We present the Materials Learning Algorithms (<span>MALA</span>) package, a scalable machine learning framework designed to accelerate density functional theory (DFT) calculations suitable for large-scale atomistic simulations. Using local descriptors of the atomic environment, <span>MALA</span> models efficiently predict key electronic observables, including local density of states, electronic density, density of states, and total energy. The package integrates data sampling, model training and scalable inference into a unified library, while ensuring compatibility with standard DFT and molecular dynamics codes. We demonstrate <span>MALA</span>'s capabilities with examples including boron clusters, aluminum across its solid-liquid phase boundary, and predicting the electronic structure of a stacking fault in a large beryllium slab. Scaling analyses reveal <span>MALA</span>'s computational efficiency and identify bottlenecks for future optimization. With its ability to model electronic structures at scales far beyond standard DFT, <span>MALA</span> is well suited for modeling complex material systems, making it a versatile tool for advanced materials research.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"314 ","pages":"Article 109654"},"PeriodicalIF":7.2,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DataPix4: A C++ framework for Timepix4 configuration and read-out","authors":"Viola Cavallini ,&nbsp;Nicolò Vladi Biesuz ,&nbsp;Riccardo Bolzonella ,&nbsp;Enrico Calore ,&nbsp;Massimiliano Fiorini ,&nbsp;Alberto Gianoli ,&nbsp;Xavier Llopart Cudie ,&nbsp;Sebastiano Fabio Schifano","doi":"10.1016/j.cpc.2025.109658","DOIUrl":"10.1016/j.cpc.2025.109658","url":null,"abstract":"<div><div>DataPix4 (Data Acquisition for Timepix4 Applications) is a new C++ framework for the management of Timepix4 ASIC, a multi-purpose hybrid pixel detector designed at CERN. Timepix4 consists of a matrix of 448×512 pixels that can be connected to several types of sensors, to obtain a pixelated detector suitable for different applications. DataPix4 can be used both for the full configuration of Timepix4 and its control board, and for the data read-out via <em>slow control</em> or <em>fast links</em>. Furthermore, it has a flexible architecture that allows for changes in the hardware, making it easy to adjust the framework to custom setups and exploit all classes with minimal modification.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"314 ","pages":"Article 109658"},"PeriodicalIF":7.2,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143946996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The cumulant Green's functions method for the single impurity Anderson model","authors":"T.M. Sobreira ,&nbsp;T.O. Puel ,&nbsp;M.A. Manya ,&nbsp;S.E. Ulloa ,&nbsp;G.B. Martins ,&nbsp;J. Silva-Valencia ,&nbsp;R.N. Lira ,&nbsp;M.S. Figueira","doi":"10.1016/j.cpc.2025.109651","DOIUrl":"10.1016/j.cpc.2025.109651","url":null,"abstract":"<div><div>Using the cumulant Green's functions method (CGFM), we study the single impurity Anderson model (SIAM). The CGFM starting point is the diagonalization of the SIAM Hamiltonian expressed in a semi-chain form containing <em>N</em> sites, viz., a correlated site (simulating an impurity) connected to the remaining <span><math><mi>N</mi><mo>−</mo><mn>1</mn></math></span> uncorrelated conduction-electron sites. An exact solution can be obtained since the complete system has few sites. That solution is employed to calculate the atomic Green's functions and the approximate cumulants used to obtain the impurity and conduction Green's functions for the SIAM, and no self-consistency loop is required.</div><div>We calculated the density of states, the Friedel sum rule, and the impurity occupation number, all benchmarked against results from the numerical renormalization group (NRG). One of the main insights obtained is that, at very low temperatures, only four atomic transitions contribute to generate the entire SIAM density of states, regardless of the number of sites in the chain and the model's parameters and different regimes: Empty orbital, mixed-valence, and Kondo. We also pointed out the possibilities of the CGFM as a valid alternative to describe strongly correlated electron systems like the Hubbard and <span><math><mi>t</mi><mo>−</mo><mi>J</mi></math></span> models, the periodic Anderson model, the Kondo and Coqblin-Schrieffer models, and their variants.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"314 ","pages":"Article 109651"},"PeriodicalIF":7.2,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Structural optimization of atomic clusters using iterated dynamic lattice search: With application to silver clusters","authors":"Xiangjing Lai ,&nbsp;Jin-Kao Hao ,&nbsp;Zhaolu Guo ,&nbsp;Quan Wen ,&nbsp;Zhang-Hua Fu","doi":"10.1016/j.cpc.2025.109655","DOIUrl":"10.1016/j.cpc.2025.109655","url":null,"abstract":"<div><div>Predicting the global minimum structures of atomic clusters has important practical implications in physics and chemistry. This is because the global minimum structures of their potential function theoretically correspond to their ground state structures, which determine some important physical and chemical properties of clusters. However, this prediction task is a very challenging global optimization problem due to the fact that the number of local minima on the potential energy surface of clusters increases exponentially with the cluster size. In this study, we propose an unbiased global optimization approach, called the iterated dynamic lattice search algorithm, to search for the global minimum structure of atomic clusters. Based on the iterated local search framework, the proposed algorithm employs the well-known monotonic basin-hopping method to improve the initial structures of clusters, a surface-based perturbation operator to randomly change the positions of selected surface atoms or central atom, a dynamic lattice search method to optimize the positions of surface atoms, and the Metropolis acceptance rule to accept the optimized new solutions. The performance of the algorithm is evaluated on the 300 widely studied silver clusters and experimental results show that the proposed algorithm is highly efficient compared to the existing algorithms. In particular, the proposed algorithm improves the best-known structures for 47 clusters and matches the best-known structures for the remaining clusters. Additional experiments are performed to analyze the key components of the algorithm and the landscape of the potential energy surface of several representative clusters.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"314 ","pages":"Article 109655"},"PeriodicalIF":7.2,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143942233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implementation of asymptotic preserving discrete velocity methods into the simulation code PICLas","authors":"Félix Garmirian,&nbsp;Marcel Pfeiffer","doi":"10.1016/j.cpc.2025.109648","DOIUrl":"10.1016/j.cpc.2025.109648","url":null,"abstract":"<div><div>The Bhatnagar-Gross-Krook (BGK) model of the Boltzmann equation allows for efficient flow simulations, especially in the transition regime between continuum and high rarefaction. However, ensuring efficient performances for multiscale flows, in which the Knudsen number varies by several orders of magnitude, is never straightforward. Discrete velocity methods as well as particle-based solvers can each reveal advantageous in different conditions, but not without compromises in specific regimes. This article presents a second-order asymptotic preserving discrete velocity method to solve the BGK equation, with the particularity of maintaining positivity when operations are conducted with the cell-local distribution function. With this procedure based on exponential differencing, it is therefore also possible to construct an adapted version of this second-order method using the stochastic particle approach, as presented in Pfeiffer et al. <span><span>[1]</span></span>. The deterministic variant and its implementation are detailed here and its performances are evaluated on several test cases. Combined to the probabilistic solver and with the possibility of a future coupling, our exponential differencing discrete velocity method provides a robust toolbox, useful for efficiently simulating multiscale gas phenomena.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"314 ","pages":"Article 109648"},"PeriodicalIF":7.2,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143934608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}