  Previous attempts at segmenting molecular line maps of molecular clouds have
focused on using position-position-velocity data cubes of a single line to
separate the spatial components of the cloud. In contrast, wide field spectral
imaging with large spectral bandwidth in the (sub)mm domain now allows to
combine multiple molecular tracers to understand the different physical and
chemical phases that constitute giant molecular clouds. We aim at using
multiple tracers (sensitive to different physical processes) to segment a
molecular cloud into physically/chemically similar regions (rather than
spatially connected components). We use a machine learning clustering method
(the Meanshift algorithm) to cluster pixels with similar molecular emission,
ignoring spatial information. Simple radiative transfer models are used to
interpret the astrophysical information uncovered by the clustering. A
clustering analysis based only on the J=1-0 lines of 12CO, 13CO and C18O
reveals distinct density/column density regimes (nH~100, 500, and >1000 cm-3),
closely related to the usual definitions of diffuse, translucent and
high-column-density regions. Adding two UV-sensitive tracers, the (1-0) lines
of HCO+ and CN, allows us to distinguish two clearly distinct chemical regimes,
characteristic of UV-illuminated and UV-shielded gas. The UV-illuminated regime
shows overbright HCO+ and CN emission, which we relate to photochemical
enrichment. We also find a tail of high CN/HCO+ intensity ratio in
UV-illuminated regions. Finer distinctions in density classes (nH~7E3, and 4E4
cm-3) for the densest regions are also identified, likely related to the higher
critical density of the CN and HCO+ (1-0) lines. The association of
simultaneous multi-line, wide-field mapping and powerful machine learning
methods such as the Meanshift algorithm reveals how to decode the complex
information available in molecular tracers.
