A New Framework for Multiline Analysis Combined Kernel Principal Component Analysis and Kernel Shapley Additive Explanations: A Case of NGC 1068 ALMA Band 3 Data

We present a new framework for multiline analysis that combines kernel principal component analysis (Kernel PCA), an unsupervised machine learning method, and Kernel Shapley Additive Explanations (SHAP), an explainable artificial intelligence technique. To enable a comparison with PCA-based studies, which have been widely used in multiline analyses, we apply our framework to integrated intensity maps of 13 molecular lines from Atacama Large Millimeter/submillimeter Array Band 3 archival data of the nearby galaxy NGC 1068. Previous PCA-based studies of NGC 1068 reported that physically meaningful structures are mainly captured up to the second component. In contrast, our framework suggests that the features up to the third component are physically meaningful, whereas the physical meaning of the fourth component still requires further discussion. Furthermore, by comparing the results obtained from our framework with molecular column densities derived from local thermodynamical equilibrium analysis, we suggest that the abundance of HCO + may be relatively enhanced in the molecular outflow region extending to a radius of about 400 pc from the galactic center, likely due to the effects of ultraviolet radiation and highly dense gas. These results show that our framework can provide data-driven insights into physical and chemical features that have not been clearly identified in previous studies. It also provides an efficient tool for interpreting the rapidly increasing amount of multiline observational data.