A CNN trained on needlet-filtered Planck-like simulations recovers CMB temperature maps with lower foreground residuals than NILC and power spectra accurate to ell about 1100.
A refined model for spinning dust radiation
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abstract
We present a comprehensive treatment of the spectrum of electric dipole emission from spinning dust grains, updating the commonly used model of Draine and Lazarian. Grain angular velocity distributions are computed using the Fokker-Planck equation; we revisit the drift and diffusion coefficients for the major torques on the grain, including collisions, grain-plasma interactions, and infrared emission. We use updated grain optical properties and size distributions. The theoretical formalism is implemented in the companion code, SPDUST, which is publicly available. The effect of some environmental and grain parameters on the emissivity is shown and analysed.
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Deep Needlet: A CNN based full sky component separation method in Needlet space
A CNN trained on needlet-filtered Planck-like simulations recovers CMB temperature maps with lower foreground residuals than NILC and power spectra accurate to ell about 1100.