Mapping synthetic observations to pre-stellar core models: An interpretable machine learning approach

Context. We present a methodology for linking the information in the synthetic spectra with the actual information in the simulated models (i.e., their physical properties), in particular to determine where the information resides in the spectra. Aims. We employed a 1D gravitational collapse model with advanced thermochemistry, from which we generated synthetic spectra. We then used neural network emulations and the SHapley Additive exPlanations (SHAP), a machine learning technique, to connect the models’ properties to the specific spectral features. Methods. Thanks to interpretable machine learning, we find several correlations between synthetic lines and some of the key model parameters, such as the cosmic-ray ionization radial profile, the central density, or the abundance of various species, suggesting that most of the information is retained in the observational process. Results. Our procedure can be generalized to similar scenarios to quantify the amount of information lost in the real observations. We also point out the limitations for future applicability.