Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · physics/0306115

Interfacing the JQMD and JAM Nuclear Reaction Codes to Geant4

T. Koi·M. Asai·D. H. Wright·K. Niita·Y. Nara·K. Amako·T. Sasaki
View original source ↗Read source document ↗Export metadata ↓

Abstract

Geant4 is a toolkit for the simulation of the passage of particles through matter. It provides a comprehensive set of tools for geometry, tracking, detector response, run, event and track management, visualization and user interfaces. Geant4 also has an abundant set of physics models that handle the diverse interactions of particles with matter across a wide energy range. However, there are also many well-established reaction codes currently used in the same fields where Geant4 is applied. In order to take advantage of these codes, we began to investigate their use from within the framework of Geant4. The first codes chosen for this investigation were the Jaeri Quantum Molecular Dynamics (JQMD) and Jet AA Microscopic Transportation Model (JAM) codes. JQMD is a QMD model code which is widely used to analyze various aspects of heavy ion reactions. JAM is a hadronic cascade model code which explicitly treats all established hadronic states, including resonances with explicit spin and isospin, as well as their anti-particles. We successfully developed interfaces between these codes and Geant4. These allow a user to construct a detector using the powerful material and geometrical capabilities of Geant4, while at the same time implementing nuclear reactions handled by the JQMD and JAM models and the Hadronic framework of Geant4 proved its flexibility and expandability.

Explore related subjects

physics.comp-ph

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

T. Koi, M. Asai, D. H. Wright, K. Niita, Y. Nara, K. Amako, T. Sasaki. 2003-06-14. Interfacing the JQMD and JAM Nuclear Reaction Codes to Geant4. https://arxiv.org/abs/physics/0306115

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

A componentwise spherical Dirichlet-to-Neumann closure for finite-volume MHD at finite magnetic Reynolds number

At finite magnetic Reynolds number, the field induced by a moving conducting fluid or solid extends into open space, so a finite mesh needs an outer boundary condition that represents the decaying exterior field. Magnetohydrodynamic (MHD) codes widely use pseudo-vacuum or truncation at a finite radius. Neither condition is physically correct for a vacuum exterior, but the exact alternatives are costly or limited to simple geometries. We present a Dirichlet-to-Neumann (DtN) closure on a fixed enclosing sphere and release it as part of the open-source multi-region finite-volume solver FreeMHD. The closure matches each Cartesian component of the vector potential to its decaying exterior solution, which retains all three decaying vector-Laplace families. A meshed vacuum buffer connects non-spherical conductors to the sphere. We verify the closure with manufactured modes, sphere free decay, sphere and oblate dynamos, and a freely braking rotor. Under refinement, oblate dynamo thresholds and sphere growth rates converge toward independent spectral references, and the rotor torque matches an exact transient reference. On the same sphere, pseudo-vacuum and truncation give the larger errors predicted by their exterior models.

physics.comp-ph↗
arXiv2026

Integration of Silica in G4CMP for Phonon Simulations: Framework and Tools for Material Integration

Superconducting detectors with sub-eV energy resolution have demonstrated success setting limits on Beyond the Standard Model (BSM) physics due to their unique sensitivity to low-energy events. G4CMP, a Geant4-based extension for condensed matter physics, provides a comprehensive toolkit for modeling phonon and charge dynamics in cryogenic materials. This paper introduces a technical formalism to support the superconducting qubit and low-threshold detector community in implementing phonon simulations in custom materials into the G4CMP. As a case study, we present the results of a detailed analysis of silica phonon transport properties relevant for simulating substrate backgrounds in Beryllium Electron capture in Superconducting Tunnel junctions (BeEST)-style experiments using G4CMP. Additionally, Python-based tools were developed to aid users in implementing their own materials and are available on the G4CMP repository.

physics.comp-ph↗
arXiv2026

Discovering like a scientist: Literature-informed dimensionless learning for physical closure of data-driven equations

Data-driven methods can recover compact equations, yet the physical dependence of their coefficients across operating conditions often remains unresolved. Dimensionless groups can explain this variation, but selecting relevant groups from the many dimensionally admissible combinations remains challenging. Here, we introduce a knowledge-guided framework for dimensionless learning, called π-former. It learns patterns of variable association and exponent assignment from a corpus of 1,288 established dimensionless groups. Under exact dimensional constraints, the model proposes interpretable basic groups whose combinations are optimized against observations. Across seven physical problems, the framework identifies predictive dimensionless coordinates within seconds. Integration with equation discovery further expresses condition-dependent coefficients through these coordinates, recovering established physical scalings in multi-condition simulations. Applied to experimental measurements of evaporation-driven wetting, the framework discovers dimensionless coordinates and uses them to construct a unified trajectory equation. The resulting model generalizes to held-out conditions across liquids and initial volumes, outperforming existing empirical models. These results establish dimensionless learning as a transferable bridge between accumulated scientific knowledge and data-driven equation discovery, enabling artificial intelligence to uncover physically meaningful laws from observations.

physics.comp-ph↗