What drives the scatter of local star-forming galaxies in the BPT
  diagrams? A Machine Learning based analysis
  We investigate which physical properties are most predictive of the position
of local star forming galaxies on the BPT diagrams, by means of different
Machine Learning (ML) algorithms. Exploiting the large statistics from the
Sloan Digital Sky Survey (SDSS), we define a framework in which the deviation
of star-forming galaxies from their median sequence can be described in terms
of the relative variations in a variety of observational parameters. We train
artificial neural networks (ANN) and random forest (RF) trees to predict
whether galaxies are offset above or below the sequence (via classification),
and to estimate the exact magnitude of the offset itself (via regression). We
find, with high significance, that parameters primarily associated to
variations in the nitrogen-over-oxygen abundance ratio (N/O) are the most
predictive for the [N II]-BPT diagram, whereas properties related to star
formation (like variations in SFR or EW[H$\alpha$]) perform better in the [S
II]-BPT diagram. We interpret the former as a reflection of the N/O-O/H
relationship for local galaxies, while the latter as primarily tracing the
variation in the effective size of the S$^{+}$ emitting region, which directly
impacts the [S II]emission lines. This analysis paves the way to assess to what
extent the physics shaping local BPT diagrams is also responsible for the
offsets seen in high redshift galaxies or, instead, whether a different
framework or even different mechanisms need to be invoked.
