I will present a data-based, Machine Learning analysis aimed at identifying which physical properties are mostly connected with the position of local star forming galaxies in the classical diagnostic 'BPT' diagrams.
Exploiting the huge statistics available from spectroscopic surveys in the local Universe like the SDSS and MaNGA, I have defined a framework in which the dispersion of galaxies in the BPT diagrams and, in particular, their deviation from the local sequence best-fit, can be described by means of the relative variation in different observational properties compared to the average value retained by the bulk of the galaxies along the sequence. Artificial Neural Networks and Random Forest Trees are implemented to both classify whether galaxies lie above or below the sequence and to predict the exact distance/offset from the sequence itself. We achieve a high accuracy on the test sample in both classification and regression tasks (AUC>95%, RMSE~0.025 ), with no clear overfitting. Moreover, different approaches are implemented to rank the parameters in terms of how much informative they are for the models. We show that the nitrogen-over-oxygen abundance ratio (N/O) and the ionisation parameter (U) are the most predictive parameters in the [N II]-BPT, whereas features related to the star-forming state of galaxies perform better in the [S II]-BPT. However, we also show that both the performances and relative importance of each feature change as we consider different regions within the diagrams.
These models represent also a valuable benchmark for high redshift galaxy samples, in order to assess to what extent the physics that shape the local BPT diagrams is the same causing the offset seen in high-z sources or, instead, whether a different framework or even different physical mechanisms need to be involved.