Predicting reliable H2 column density maps from molecular line data using machine learning

Abstract

ABSTRACT The total mass estimate of molecular clouds suffers from the uncertainty in the H2-CO conversion factor, the so-called XCO factor, which is used to convert the 12CO (1–0) integrated intensity to the H2 column density. We demonstrate the machine learning’s ability to predict the H2 column density from the 12CO, 13CO, and C18O (1–0) data set of four star-forming molecular clouds: Orion A, Orion B, Aquila, and M17. When the training is performed on a subset of each cloud, the overall distribution of the predicted column density is consistent with that of the Herschel column density. The total column density predicted and observed is consistent within 10 per cent, suggesting that the machine learning prediction provides a reasonable total mass estimate of each cloud. However, the distribution of the column density for values >∼2 × 1022 cm−2, which corresponds to the dense gas, could not be predicted well. This indicates that molecular line observations tracing the dense gas are required for the training. We also found a significant difference between the predicted and observed column density when we created the model after training the data on different clouds. This highlights the presence of different XCO factors between the clouds, and further training in various clouds is required to correct for these variations. We also demonstrated that this method could predict the column density towards the area not observed by Herschel if the molecular line and column density maps are available for the small portion, and the molecular line data are available for the larger areas.