SunnyNet: A neural network approach to 3D non-LTE radiative transfer. (arXiv:2112.13852v1 [astro-ph.SR])
SunnyNet: A neural network approach to 3D non-LTE radiative transfer. (arXiv:2112.13852v1 [astro-ph.SR]) <a href="http://arxiv.org/find/astro-ph/1/au:+Chappell_B/0/1/0/all/0/1">Bruce A. Chappell</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Pereira_T/0/1/0/all/0/1">Tiago M. D. Pereira</a> Context. Computing spectra from 3D simulations of stellar atmospheres when allowing for departures from local thermodynamic equilibrium (non-LTE) is computationally very intensive. Aims. We develop a machine learning based method to speed up 3D non-LTE radiative transfer calculations in optically thick stellar atmospheres. Methods. Making use of a variety of 3D simulations of the solar atmosphere, we trained a convolutional neural network, SunnyNet, to learn the translation from LTE to non-LTE atomic populations. Non-LTE populations computed with an existing 3D code were considered asRead More →