Paper Ⅰ: https://doi.org/10.5281/zenodo.19163963

code of paper Ⅰ:https://github.com/lesleyliuhaitao-ctrl/anchor-collapse-model.git

📘 ACM Framework: The "Trunk & Audit" Approach

📘 ACM 架构:基于“主干与审计”的动力学模型

Unified Baseline, Zero Ad-hoc Parameters. 统一基线,零即兴参数。

**1. What is the ACM Trunk Function?

  1. 什么是 ACM 主干函数?**

The ACM Trunk is a multi-stage dynamical operator that predicts galactic rotation curves across all acceleration regimes. Unlike MOND, which relies on a tunable interpolating function ($\nu$), ACM uses a rigid, nested response chain. ACM 主干函数是一个多级动力学算子,用于预测所有加速度制度下的星系旋转曲线。与依赖可调插值函数 ($\nu$) 的 MOND 不同,ACM 采用的是刚性的嵌套响应链

Rigidity (刚性): The Trunk parameters are fixed across the entire 164-galaxy sample. 主干参数在整个 164 个星系样本中是完全固定的。

Predictability (可预测性): Any deviation from the Trunk is treated as a "Data Pathology" to be audited, not a "Model Failure" to be patched. 任何偏离主干的情况都被视为需要审计的“数据病理”,而非需要打补丁的“模型失效”。

**2. The Functional Hierarchy

  1. 函数的层级结构**

The Trunk Function processes the baryonic distribution through three logical layers: 主干函数通过三个逻辑层处理重子分布:

**3. Why it outperforms MOND

  1. 为什么它优于 MOND**
Feature 特性 MOND ACM
Logic 逻辑 Curve-fitting (曲线拟合) Forensic Audit (法医审计)
Parameters 参数 $a_0$ + Interpolation (可调) Rigid Trunk (固定主干)
Outliers 离群值 Ignored or patched (忽略或打补丁) Diagnosed as Pathology (诊断为病理)
Consistency 一致性 Galaxy-dependent (依赖具体星系) Universal Baseline (全局统一基线)

**4. The 3-Step Audit Workflow

  1. 三步审计工作流**

When a galaxy shows a high residual against the ACM Trunk, we perform a Forensic Audit: 当星系对 ACM 主干表现出高残差时,我们进行法医审计