GroArnold: A Framework for Constructing Action-based Models for Triaxial Galaxies and Dark Matter Halos
Abstract
Action-based distribution functions f(J) provide a powerful description of stellar systems. Consequently, the construction of physically accurate and dynamically stable f(J) for galactic components has emerged as a key challenge in the field. In this study, we present GroArnold, a computational framework designed to construct analytical f(J) expressions for axisymmetric and triaxial galaxies and dark matter halos. The framework integrates modules for N-body initial condition generation, N-body simulation, angle--action variable estimation, and distribution-function fitting into a unified workflow. It supports a wide range of density profiles and kinematic properties, and is uniquely designed to generate stable N-body models for user-specified galactic systems, with the aim of obtaining an f(J) that approximates a dynamically stable and physically consistent state. To this end, we extend the triaxial Stäckel Fudge method of the TACT code, implemented in our Nbody-TACT module, to enable action estimation directly from N-body gravitational potentials. GroArnold provides a robust and flexible tool for deriving numerically empirical, explicitly analytical distribution functions for user-specified galaxies or halos, thereby facilitating a wide range of practical f(J)-based modeling applications. In addition, it serves as a powerful tool for investigating the dynamics of realistic triaxial galaxies and halos beyond the scope of traditional spherical or axisymmetric models. We assessed the accuracy of the output f(J) by comparing its predicted spatial and velocity distributions with those of the input N-body snapshot, and confirmed that it provides a reasonably accurate representation of the input. The code is modular, publicly available, and designed to support future extensions and adaptations.
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BibTeX
@misc{gu2026groarnold,
title = {{GroArnold: A Framework for Constructing Action-based Models for Triaxial Galaxies and Dark Matter Halos}},
author = {Jian-Yu Gu and Xiao-Bo Dong and Shi-Yan Zhong and Run-Yu Meng},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.18978},
eprint = {2610.18978},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.18978},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - GroArnold: A Framework for Constructing Action-based Models for Triaxial Galaxies and Dark Matter Halos
AU - Jian-Yu Gu
AU - Xiao-Bo Dong
AU - Shi-Yan Zhong
AU - Run-Yu Meng
PY - 2026
DA - 2026/10/29
PB - arXiv
DO - 10.48550/arxiv.2610.18978
UR - https://arxiv.org/abs/2610.18978
AB - Action-based distribution functions f(J) provide a powerful description of stellar systems. Consequently, the construction of physically accurate and dynamically stable f(J) for galactic components has emerged as a key challenge in the field. In this study, we present GroArnold, a computational framework designed to construct analytical f(J) expressions for axisymmetric and triaxial galaxies and dark matter halos. The framework integrates modules for N-body initial condition generation, N-body simulation, angle--action variable estimation, and distribution-function fitting into a unified workflow. It supports a wide range of density profiles and kinematic properties, and is uniquely designed to generate stable N-body models for user-specified galactic systems, with the aim of obtaining an f(J) that approximates a dynamically stable and physically consistent state. To this end, we extend the triaxial Stäckel Fudge method of the TACT code, implemented in our Nbody-TACT module, to enable action estimation directly from N-body gravitational potentials. GroArnold provides a robust and flexible tool for deriving numerically empirical, explicitly analytical distribution functions for user-specified galaxies or halos, thereby facilitating a wide range of practical f(J)-based modeling applications. In addition, it serves as a powerful tool for investigating the dynamics of realistic triaxial galaxies and halos beyond the scope of traditional spherical or axisymmetric models. We assessed the accuracy of the output f(J) by comparing its predicted spatial and velocity distributions with those of the input N-body snapshot, and confirmed that it provides a reasonably accurate representation of the input. The code is modular, publicly available, and designed to support future extensions and adaptations.
ER -