Neural Network Predictions
Based on supervised deep learning algorithms, the CADE service produced dust emission estimates at 850 microns and 1.38 mm at the Herschel resolution (37 arcsec). To perform this work, Herschel data of Galactic environments, smoothed to the 5 arcmin angular resolution, have been used to train the neural network to provide the best model able to reproduce Planck maps of dust emission (after removing CMB, CIB and 12CO contributions) at these two previous wavelengths. Then, using Herschel data only, the model is applied to predict dust emission maps at 37 arcsec.
The service provides to the community dust emission maps of several surveys: Hi-GAL, Gould Belt, Cold Cores, HERITAGE, KINGFISH, Helga, HerM33es and VNGS.
The units of the Healpix maps are MJy/sr.
References
- 'title' by Paradis, D. et al, 2024, A&A,