Inferring the dust emission at submillimeter and millimeter wavelengths using neural networks

Abstract

The Planck mission provided all-sky dust emission maps in the submm to mm range at an angular resolution of 5'. In addition, some specific sources can be observed at long wavelengths and higher resolution using ground-based telescopes. These observations are limited to small scales and require extensive data processing before they become available for scientific analysis. They also suffer from extended emission filtering. At present, we are still unable to fully understand the emissivity variations observed in different astrophysical environments at long wavelengths. It is therefore challenging to estimate any dust emission in the submm-mm at a better resolution than the 5' from Planck. In this analysis, based on supervised deep learning algorithms, we produced dust emission predictions in the two Planck bands centered at 850 mic and 1.38 mm, at the Herschel resolution (37''). Herschel data of Galactic environments, ranging from 160 to 500 mic and smoothed to 5', were used to train the neural network. Then, using Herschel data only, the model was applied to predict dust emission maps at 37''. The neural network is capable of reproducing dust emission maps of various Galactic environments. Remarkably, it also performs well for nearby extragalactic environments. This could indicate that large dust grains have similar properties in both our Galaxy and nearby galaxies, or at least that their spectral behaviors are comparable in Galactic and extragalactic environments. We provide dust emission prediction maps at 850 mic and 1.38 mm at the 37'' of several surveys: Hi-GAL, Gould Belt, Cold Cores, HERITAGE, Helga, HerM33es, KINGFISH, and VNGS. The ratio of these two wavelength brightness bands reveals a derived emissivity spectral index statistically close to 1 for all the surveys, which favors the hypothesis of a flattened dust emission spectrum for wavelengths larger than 850 mic.