📝 SummarXiv

Abstract

With the rise of the integral field spectroscopy, we are currently dealing with large amounts of spatially resolved data, whose analysis has become challenging, especially when observing complex objects such as nearby galaxies. We aim to develop a method to automatically separate different physical regions within the central parts (1"~160 pc, on average) of galaxies. This can allow us to better understand the systems, and provide an initial characterisation of the main ionisation sources affecting its evolution. We have developed an unsupervised hierarchical clustering algorithm to analyse data cubes based on spectral similarity. It clusters together spaxels with similar spectra, which is useful to disentangle between different physical processes. We have applied this method to a sample of 15 nearby (distances <100 Mpc) galaxies, 7 from the Galaxy Activity, Torus, and Outflow Survey (GATOS) and 8 archival sources, all observed with the medium resolution spectrometer (MRS) of the Mid-Infrared Instrument (MIRI) on board of the JWST. From the clusters, we computed their median spectrum and measured the line and continuum properties. We used these measurements to train random forest models and create several empirical mid-IR diagnostic diagrams for the MRS channel 3 wavelength range, including among others the bright [Ne II], [Ne III], and [Ne V] lines, several H2 transitions, and PAH features. The clustering technique allows to differentiate emission coming from an AGN, the disc, and star forming regions in galaxies, and other composite regions, potentially ionised by several sources simultaneously. This is supported by the results from the empirical diagnostic diagrams, that are indeed able to separate physically distinct regions. This innovative method serves as a tool to identify regions of interest in any data cube prior to an in-depth analysis of the sources. [abridged]