Predicting Dust Temperature from Molecular Line Data Using Machine Learning

Abstract We have conducted experiments with machine learning techniques to construct dust temperature maps from the CO isotopolog molecular line data in the Orion A molecular cloud. In classical astrophysical methodology, multiband continuum data are required to derive the dust temperature. The present study aims to investigate the capability and limitations of machine learning techniques in deriving dust temperatures in regions without multiband dust continuum data. We have investigated how the number of pixels used for training influences the prediction accuracy and how the dust temperatures sampled in the training area influence the prediction accuracy. We find that ∼5% of the total number of pixels in the observational region is sufficient for training to obtain accurate predictions. Furthermore, a dust temperature sample within the training area should cover the whole temperature range and have a similar sample distribution to that of the entire observing region for an accurate prediction. The 12 CO/ 13 CO ratio is often found to be the most important feature in predicting the dust temperature. As the 12 CO/ 13 CO ratio is a tracer of photon-dominated regions (PDRs), the machine learning technique could connect the dust temperatures to PDRs. We also find that the condition of the thermal gas–dust coupling is not required for the accurate prediction of the dust temperature from the molecular line data and that machine learning is capable of capturing more information than classical astrophysical concepts.