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":"A new CUDA improved and optimised version announcement of the quantum dissipative dynamics package","authors":"Jordan Heraud ,&nbsp;Marc Vincendon ,&nbsp;Paul-Gerhard Reinhard ,&nbsp;Eric Suraud ,&nbsp;Phuong Mai Dinh","doi":"10.1016/j.cpc.2025.109677","DOIUrl":"10.1016/j.cpc.2025.109677","url":null,"abstract":"<div><div>The third release of the Quantum Dissipative Dynamics (QDD) package follows the second release [P. M. Dinh, et al., <em>Comp. Phys. Comm.</em> 295 (2024) 108947] in which the focus was mainly made on the optimization of the electronic dynamics, in particular with a CUDA fortran coding to allow the use of a GPU. In this new release, we pursued the exportation of other parts of QDD on CUDA-capable GPUs, as the ionic motion, the coupling of the electrons with a laser field and/or with the ions (via pseudopotentials), and all electronic observables, including the involved photo-electron spectra, energy- and/or angle-resolved. Several specific CUDA optimisations have also been implemented, to improve the performance and the memory usage while keeping the accuracy of the results.</div></div><div><h3>NEW VERSION PROGRAM SUMMARY</h3><div><em>Program Title:</em> QDD</div><div><em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/2fg47zm4mz.1</span><svg><path></path></svg></span></div><div><em>Licensing provisions:</em> GPLv3</div><div><em>Programming language:</em> Fortran 90, CudaFortran</div><div><em>Journal reference of previous version:</em> P. M. Dinh, et al., Comp. Phys. Comm. 295 (2024) 108947</div><div><em>Does the new version supersede the previous version?:</em> No</div><div><em>Reasons for the new version:</em> The QDD package was not fully ported on a CUDA-capable GPU in the second release.</div><div><em>Summary of revisions:</em> The changes in QDD are twofold. One concerns the exportation on the GPU of the computation of the ionic motion and of the computation to all observables (electronic and ionic), and some changes at the side of the calculation of the pseudopotentials. A major numerical improvement has been therefore achieved 85% of speedup. The second change consists in some pure CUDA optimization with a more modest speedup: some %, depending on the type of GPU. See Supplementary Material for more details.</div><div><em>Nature of problem:</em> Possible numerical optimizations were to be tested, at the side of the ionic motion, the coupling of the quantal electrons with external field (ions, laser), but also at the level of pure GPU usage.</div><div><em>Solution method:</em> This new version completes the exportation to a GPU by a CUDA coding of the ionic motion, the coupling of the quantal electrons with external fields (ions, laser field) and all electronic observables.</div><div><em>Additional comments including restrictions and unusual features:</em> The overall speedup can strongly depend on the type of GPU available (from an HPC centre or a workstation).</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"314 ","pages":"Article 109677"},"PeriodicalIF":7.2,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144099442","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":"FeynGame 3.0","authors":"Lars Bündgen,&nbsp;Robert V. Harlander,&nbsp;Sven Yannick Klein,&nbsp;Magnus C. Schaaf","doi":"10.1016/j.cpc.2025.109662","DOIUrl":"10.1016/j.cpc.2025.109662","url":null,"abstract":"<div><div>A major update of the program <em>FeynGame</em> is introduced. One of its main new functionalities is to visualize Feynman graphs generated by <em>qgraf</em>. The <em>qgraf</em> output can be either pasted into the <em>FeynGame</em> canvas for individual graphs, or the whole <em>qgraf</em> output file can be processed. In addition, a number of new features and improvements have been implemented into <em>FeynGame 3.0</em> in order to further facilitate the efficient drawing of Feynman diagrams in publication quality. <em>FeynGame</em> is freely available<ul><li><span>•</span><span><div>as <span>jar</span> or MacOS <span>app</span> file from</div><div><span><span>https://web.physik.rwth-aachen.de/user/harlander/software/feyngame</span><svg><path></path></svg></span></div></span></li><li><span>•</span><span><div>as source code from <span><span>https://gitlab.com/feyngame/FeynGame</span><svg><path></path></svg></span></div></span></li></ul></div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"314 ","pages":"Article 109662"},"PeriodicalIF":7.2,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144106826","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":"Goupil: A Monte Carlo engine for the backward transport of low-energy gamma-rays","authors":"Valentin Niess ,&nbsp;Kinson Vernet ,&nbsp;Luca Terray","doi":"10.1016/j.cpc.2025.109653","DOIUrl":"10.1016/j.cpc.2025.109653","url":null,"abstract":"<div><div><span>Goupil</span> is a software library designed for the Monte Carlo transport of low-energy gamma-rays, such as those emitted from radioactive isotopes. The library is distributed as a Python module. It implements a dedicated backward sampling algorithm that is highly effective for geometries where the source size largely exceeds the detector size. When used in conjunction with a conventional Monte Carlo engine (i.e., <span>Geant4</span>), the response of a scintillation detector to gamma-active radio-isotopes scattered over the environment is accurately simulated (to the nearest percent) while achieving events rates of a few kHz (with a ∼2.3<!--> <!-->GHz CPU).</div></div><div><h3>Program summary</h3><div><em>Program Title:</em> Goupil</div><div><em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/r2m8mr9jnk.1</span><svg><path></path></svg></span></div><div><em>Developer's repository link:</em> <span><span>https://github.com/niess/goupil</span><svg><path></path></svg></span></div><div><em>Licensing provisions:</em> LGPL-3.0</div><div><em>Programming language:</em> C, Python and Rust.</div><div><em>Nature of problem:</em> Backward Monte Carlo transport of gamma-rays that are emitted by mono-energetic sources distributed in space.</div><div><em>Solution method:</em> A simple modification to a previously presented backward Monte Carlo algorithm [1].</div></div><div><h3>References</h3><div><ul><li><span>[1]</span><span><div>V. Niess, A. Barnoud, C. Cârloganu, E. Le Ménédeu, Comput. Phys. Commun. 229 (2018) 54–67, <span><span>https://doi.org/10.1016/j.cpc.2018.04.001</span><svg><path></path></svg></span>.</div></span></li></ul></div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"314 ","pages":"Article 109653"},"PeriodicalIF":7.2,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144090395","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}