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

Abstract

The Planck mission provided all-sky dust emission maps in the submillimeter (submm) to millimeter (mm) range at an angular resolution of 5$^ prime $. In addition, some specific sources can be observed at long wavelengths and higher resolution using ground-based telescopes. These observations are limited to small scales and are sometimes not delivered to the community. These ground-based observations require extensive data processing before they become available for scientific analysis, and suffer from extended emission filtering. At present, we are still unable to fully understand the emissivity variations observed in different astrophysical environments at long (submm and mm) wavelengths. Several models have been developed to reproduce the diffuse Galactic medium, and each distinct environment requires an adjustment of the models. It is therefore challenging to estimate any dust emission in the submm-mm at a better resolution than the 5$^ prime $ 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 (353 GHz) and 1.38 mm (217 GHz) at the Herschel resolution (37$^ prime $). Prediction or forecasting is a frequently used term in machine learning or neural network research that refers to the output of an algorithm that has been trained on a given dataset and that is being used for modeling purposes. Herschel data of Galactic environments, ranging from 160 mic to 500 mic and smoothed to an angular resolution of 5$^ prime $, were used to train the neural network. This training aimed to provide the most accurate model for reproducing Planck maps of dust emission at 850 mic and 1.38 mm. Then, using Herschel data only, the model was applied to predict dust emission maps at 37$^ prime The neural network is capable of reproducing dust emission maps of various Galactic environments with a difference of only a few percent at the Planck resolution. Remarkably, it also performs well for nearby extragalactic environments. This could indicate that large dust grains, probed by submm or mm observations, have similar properties in both our Galaxy and nearby galaxies, or at least that their spectral behaviors are comparable in Galactic and extragalactic environments. For the first time, we provide to the community dust emission prediction maps at 850 mic and 1.38 mm at the 37$^ prime $ of several surveys: Hi-GAL, Gould Belt, Cold Cores, HERITAGE, Helga, HerM33es, KINGFISH, and Very Nearby Galaxies. 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 . Neural networks appear to be powerful algorithms that are highly efficient at learning from large datasets and achieving accurate reproductions with a deviation of only a few percent. However, to fully recover the input data during the training, it is essential to sample a sufficiently large range of datasets and physical conditions.