用因果发现方法解析星系物理中的隐藏机制
The causal structure of galactic astrophysics
- 基于因果发现算法挖掘星系数据中变量的直接关联与方向
- 在45万星系数据上区分仅靠相关性无法分辨的物理机制
- 适合从事数据驱动天体物理研究的学者参考
当前数据驱动的天体物理主要依赖于观测属性间的相关性检测与表征，进而验证物理理论。然而，这一过程未能利用理论预测中至关重要的信息：哪些变量存在直接关联（而非通过其他变量间接相关）、关联的方向性，以及数据中缺失但影响变量关系的混杂因子。本文提出通过因果发现方法恢复这些信息。因果发现是一种成熟的推断数据因果结构的方法，在天体物理领域几乎无人知晓。我们开发了适用于大规模天体物理数据集的因果发现算法，并在来自NASA Sloan Atlas的约4.5×10⁵个邻近星系数据上进行了演示，证明其能有效区分仅凭相关性无法分辨的物理机制。
原文摘要 · Abstract (English)
Data-driven astrophysics currently relies on the detection and characterisation of correlations between objects' properties, which are then used to test physical theories that make predictions for them. This process fails to utilise information in the data that forms a crucial part of the theories' predictions, namely which variables are directly correlated (as opposed to accidentally correlated through others), the directions of these determinations, and the presence or absence of confounders that correlate variables in the dataset but are themselves absent from it. We propose to recover this information through causal discovery, a well-developed methodology for inferring the causal structure of datasets that is however almost entirely unknown to astrophysics. We develop a causal discovery algorithm suitable for large astrophysical datasets and illustrate it on $\sim$4.5$\times10^5$ nearby galaxies from the Nasa Sloan Atlas, demonstrating its ability to distinguish physical mechanisms that are degenerate on the basis of correlations alone.
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