Machine learning-accelerated chemistry modeling of protoplanetary disks
This provides a faster method for astronomers to analyze molecular emission data from observatories, though it is incremental as it applies an existing machine learning technique to a specific domain problem.
The researchers tackled the need for fast forward models of chemical composition in protoplanetary disks by training a K-nearest neighbors regressor to instantly predict chemistry from a small subset of physical conditions, achieving accurate reproduction of chemistry.
Aims. With the large amount of molecular emission data from (sub)millimeter observatories and incoming James Webb Space Telescope infrared spectroscopy, access to fast forward models of the chemical composition of protoplanetary disks is of paramount importance. Methods. We used a thermo-chemical modeling code to generate a diverse population of protoplanetary disk models. We trained a K-nearest neighbors (KNN) regressor to instantly predict the chemistry of other disk models. Results. We show that it is possible to accurately reproduce chemistry using just a small subset of physical conditions, thanks to correlations between the local physical conditions in adopted protoplanetary disk models. We discuss the uncertainties and limitations of this method. Conclusions. The proposed method can be used for Bayesian fitting of the line emission data to retrieve disk properties from observations. We present a pipeline for reproducing the same approach on other disk chemical model sets.