Computer Physics Communications

The version 1.2 of the HepLib (a C++ Library for computations in High Energy Physics) is presented. HepLib builds on top of other well-established libraries or programs, including GINAC, FLINT, FORM, FIRE, etc., its first version has been released in Comput. Phys. Commun. 265, 107,982 (2021). Here we provide another minor upgraded version 1.2, in which the internal depended libraries or programs are updated to their latest versions, several bugs are fixed, many functional performances are improved, and lots of new features are also introduced. We also carry out experimental tests on the program FIRE, employing FLINT to enhance its performance with multivariate polynomials in the integrate-by-parts (IBP) reduction.

Simulations of hadronic and nuclear interactions are essential in both collider and astroparticle physics. The Chromo package provides a unified Python interface to multiple widely used hadronic event generators, including EPOS, DPMJet, Sibyll, QGSJet, and Pythia. Built on top of their original Fortran and C++ implementations, Chromo offers a zero-overhead abstraction layer suitable for use in Python scripts, Jupyter notebooks, or from the command line, while preserving the performance of direct calls to the generators. It is easy to install via precompiled binary wheels distributed through PyPI, and it integrates well with the Scientific Python ecosystem. Chromo supports event export in HepMC, ROOT, and SVG formats and provides a consistent interface for inspecting, filtering, and modifying particle collision events. This paper describes the architecture, typical use cases, and performance characteristics of Chromo and its role in contemporary astroparticle simulations, such as in the MCEq cascade solver.

Analytic continuation is an essential step in quantum Monte Carlo calculations. We present version 2.0 of the ACFlow package, a full-fledged open source toolkit for analytic continuation of quantum Monte Carlo simulation data. The new version adds support for three recently developed analytic continuation methods, namely the barycentric rational function approximation method, the stochastic pole expansion method, and the Nevanlinna analytical continuation method. The well-established maximum entropy method is also enhanced with the Bayesian reconstruction entropy algorithm. Furthermore, a web-based graphical user interface and a testing toolkit for analytic continuation methods are introduced. In this paper, we at first summarize the basic principles of the newly implemented analytic continuation solvers, and the most important improvements of ACFlow 2.0. Then a representative example is provided to demonstrate the new usages and features.

Theoretical methods based on the density matrix provide powerful tools for describing open quantum systems. However, such methods are complicated and intricate to be used analytically. Here we present an object-oriented framework for constructing the equation of motion of the correlation matrix at a given order within the quantum BBGKY hierarchy, which is widely used to describe the interaction of many-particle systems. The algorithm of machine derivation of equations includes the implementation of the principles of quantum mechanics and operator algebra. It is based on the description and use of classes in the Python programming environment. Class objects correspond to the elements of the equations that are derived: density matrix, correlation matrix, energy operators, commutator and several operators indexing systems. The program contains a special class that allows one to define a statistical ensemble with an infinite number of subsystems. For all classes, methods implementing the actions of the operator algebra are specified. The number of subsystems of the statistical ensemble for the physical problem and the types of subsystems between which pairwise interactions are possible are specified as an input parameters. It is shown that this framework allows one to derive the equations of motion of the fourth-order correlation matrix in less than one minute.

The permanent is a function, defined for a square matrix, with applications in various domains including quantum computing, statistical physics, complexity theory, combinatorics, and graph theory. Its formula is similar to that of the determinant; however, unlike the determinant, its exact computation is #P-complete, i.e., there is no algorithm to compute the permanent in polynomial time unless P=NP. For an n × n matrix, the fastest algorithm has a time complexity of O(2^(n-1) n). Although supercomputers have been employed for permanent computation before, there is no work and, more importantly, no publicly available software that leverages cutting-edge High-Performance Computing accelerators such as GPUs. In this work, we design, develop, and investigate the performance of SUperman, a complete software suite that can compute matrix permanents on multiple nodes/GPUs on a cluster while handling various matrix types, e.g., real/complex/binary and sparse/dense, etc., with a unique treatment for each type. SUperman run on a single Nvidia A100 GPU is up to 86 ×  faster than a state-of-the-art parallel algorithm on 44 Intel Xeon cores running at 2.10GHz. Leveraging 192 GPUs, SUperman computes the permanent of a 62 × 62 matrix in 1.63 days, marking the largest reported permanent computation to date.

DynHeMat is a parallel program aimed at modeling the dynamics of pure and doped helium nanodroplets (HNDs) by means of zero-point averaged dynamics (ZPAD), a method where the quantum nature of helium atoms is taken into account through the use of a He-He pseudopotential which includes zero-point effects of helium clusters on an average manner. Three He-He pseudopotentials, defined for applications in different contexts, are implemented. Large HNDs can be formed by successive coalescences of smaller HNDs keeping in mind that, depending on the HND size and He-He pseudopotential in use, the liquid character of the HND is more or less pronounced. Files containing the positions and velocities of HNDs formed with the three aforementioned He-He pseudopotentials are collected in a local databank, called ZPAD_DB. ZPAD simulations can be carried out at constant energy or temperature, then enabling the user to investigate collision, coagulation or submersion processes in pure or doped HNDs. Impurities can be rare-gas atoms (Ne, Ar, Kr, Xe and Rn), alkali atoms (Li, Na, K, Rb, Cs), or homogeneous clusters composed of such atoms. The program provides information on trajectories, namely positions, velocities, energies, radial distribution functions, and the initial distribution of HND surface atoms. Extension to other impurities or He-He pseudopotentials is made possible by the current structure of the program and keyword system.

Many first-principles packages employ periodic and symmetry conditions to reduce the computational time and cost. The supercell (SC) method is useful to address periodic systems with different physical perturbations; however, the theoretical definition of a specific SC is a real challenge in Crystallography and Solid State Physics studies. In particular, whether the system is commensurable and made of several two-dimensional (2D) layers with different Bravais lattice, initial local stacking, and interlayer relative orientation. This work presents Nookiin (from the junction of Yucatec Maya words, Nook: ’knit’ or ’wave’; and iin: ’me’), an open-source Python code, designed for the efficient generation of commensurable SCs using geometric methods. Nookiin has an efficient algorithm that minimizes structural distortions at a geometric level, providing an optimized approach for representing 2D heterostructures with a reduced number of atoms. Its modular architecture facilitates adaptation to different problems. Its use through both an interactive console interface and programmatic implementation allows seamless integration into scientific workflows. Additionally, Nookiin offers tools for structural visualization and export of configurations compatible with first-principles codes such as the Vienna ab initio Simulation Package (VASP) code [17]. This report presents the theoretical foundations of the method, the computational implementation of the algorithm, and the results obtained that validate its effectiveness in generating commensurable SCs. With these characteristics, Nookiin establishes itself as a versatile and alternative resource for research in Solid State Physics and Materials Science. The software is openly available at github.com/OssielAg/Nook-iin, with a citable release archived at doi.org/10.5281/zenodo.15706528.

We present a new simulation framework for the detection of aerosol fluorescence with integration spheres. Utilizing a Monte Carlo based ray-tracing approach, aerosol fluorescence within integrating sphere setups is simulated from photon generation through laser excitation over interactions with the setup components to losses and finally detection. Through modular design, the position and number of openings, sensors, etc. can be freely configured. Therefore, potential experimental setups can be evaluated with regard to overall performance, bottlenecks can be identified and the impact of different component parameters determined.

M2C (Multiphysics Modeling and Computation) is an open-source software for simulating multi-material fluid flows and fluid-structure interactions under extreme conditions, such as high pressures, high temperatures, shock waves, and large interface deformations. It employs a finite volume method to solve the compressible Navier-Stokes equations and supports a wide range of thermodynamic equations of state. M2C incorporates models of laser radiation and absorption, phase transition, and ionization, coupled with continuum dynamics. Multi-material interfaces are evolved using a level set method, while fluid-structure interfaces are tracked using an embedded boundary method. Advective fluxes across interfaces are computed using FIVER (FInite Volume method based on Exact multi-material Riemann problems). For two-way fluid-structure interaction, M2C is coupled with the open-source structural dynamics solver Aero-S using a partitioned procedure. The M2C code is written in C++ and parallelized with MPI for high-performance computing. The source package includes a set of example problems for demonstration and user training. Accuracy is verified through benchmark cases such as Riemann problems, interface evolution, single-bubble dynamics, and ionization response. Several multiphysics applications are also presented, including laser-induced thermal cavitation, explosion and blast mitigation, and hypervelocity impact.

We present a MATLAB script, atomiongpu.m, which can use GPU parallelization to run several million independent simulations per day of a trapped ion interacting with a low-density cloud of atoms, calculating classical trajectories of a trapped ion and an atom starting far away. The script uses ode45gpu, which is our optimized and specialized implementation of the Runge-Kutta algorithm used in MATLAB’s ODE solver ode45. We first discuss the physical system and show how ode45gpu can, on a CPU, solve it about 7x faster than MATLAB’s ode45, leading to a 600x-3500x speedup when running a million trajectories using ode45gpu in parallel on a GPU compared to ode45 on a CPU. Then, we show how to easily modify the inputs to atomiongpu.m to account for different kinds of atoms, ions, atom-ion interactions, trap potentials, simulation parameters, initial conditions, and computational hardware, so that atomiongpu.m automatically finds the probability of complex formation, the distribution of observables such as the scattering angle and complex lifetime, and plots of specific trajectories.