AI 中文总结
该研究基于双组分维里定理框架，构建常数相互作用近似模型与维里驱动相互作用模型，解释星系与星系团的径向加速度关系，覆盖宽质量与加速度范围。
AI 中文摘要
我们对来自多个观测来源的引力系统加速度数据开展了对比分析，涵盖：(i)早型星系（ETGs，Lelli等人2017年研究）；(ii)最亮团星系（BCGs）与星系团（Tian等人2020、2024年研究）；(iii)孤立星系的弱引力透镜数据（Brouwer等人2021年研究、Mistele等人2024年研究）。这些数据在由双组分维里定理（2VT）驱动的框架内进行解读，该框架定义了重子-暗物质（DM）的全局耦合关系并设定了特征加速度标度。此基线补充了两个模型，用于解释径向加速度关系（RAR）在宽质量与加速度范围内的主要经验特征。常数相互作用近似模型（CIA）可复现ETGs、BCGs及星系团观测到的RAR趋势，拓展了Dantas等人2000、2018年的早期研究成果，且能解释小内禀散度与特征加速度标度的出现。但在弱透镜数据探测到的极低加速度（低于10⁻¹⁴ m·s⁻²量级）下，该模型失效；此时，维里驱动相互作用模型（VIM）通过引入与半径相关的局域贡献来体现重子-暗物质相互作用的径向结构。综上，2VT（全局标度）、CIA与VIM共同构成了一个符合物理动机的框架，可捕捉RAR的主要经验特征。
英文摘要
We present a comparative analysis of acceleration data for gravitational systems drawn from multiple observational sources, including: (i) early-type galaxies (ETGs) (Lelli et al. 2017); (ii) Brightest Cluster Galaxies (BCGs) and galaxy clusters (Tian et al. 2020, 2024); and (iii) weak gravitational lensing of isolated galaxies (Brouwer et al. 2021; Mistele et al. 2024). These data are interpreted within a framework motivated by the Two-component Virial Theorem (2VT), which defines a global baryon-dark matter (DM) coupling and sets a characteristic acceleration scale. This baseline is complemented by two models that account for the main empirical features of the Radial Acceleration Relation (RAR) over a broad range of masses and accelerations. The Constant-Interaction Approximation Model (CIA) reproduces the observed RAR trends for ETGs, BCGs, and galaxy clusters. It extends earlier results (Dantas et al. 2000, 2018), and accounts for both the small intrinsic scatter and the emergence of a characteristic acceleration scale. At the very low accelerations probed by weak-lensing data (below accelerations of order $10^{-14}~\mathrm{m\,s^{-2}}$), however, this model breaks down. In this regime, the Virial-Motivated Interaction Model (VIM) incorporates the radial structure of the baryon-DM interaction through a local, radius-dependent contribution to the acceleration. Taken together, the 2VT (global scale), the CIA and the VIM provide a physically motivated framework that captures the main empirical features of the RAR.
Comments21 pages, 4 figures, Appendix, accepted for publication in ApJ
