Mapping Synthetic Observations to Prestellar Core Models: An Interpretable Machine Learning Approach

TL;DR

This paper introduces an interpretable machine learning method that links synthetic spectra from prestellar core models to their physical properties, revealing how spectral features encode core characteristics.

Contribution

It presents a novel approach combining neural networks and SHAP to interpret spectral data and identify key physical parameters in prestellar core models.

Findings

Spectral features correlate with core properties like density and cosmic-ray ionization.

Most information about physical properties is retained in the synthetic spectra.

The method can quantify information loss in real observations.

Abstract

Observations of molecular lines are a key tool to determine the main physical properties of prestellar cores. However, not all the information is retained in the observational process or easily interpretable, especially when a larger number of physical properties and spectral features are involved. We present a methodology to link 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. We employ a 1D gravitational collapse model with advanced thermochemistry, from which we generate synthetic spectra. We then use neural network emulations and the SHapley Additive exPlanations (SHAP), a machine learning technique, to connect the models' properties to the specific spectral features. 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. 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.