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 as the true
values. The network was then used to predict non-LTE populations for other 3D
simulations, and synthetic spectra were computed from its predicted non-LTE
populations. We used a six-level model atom of hydrogen and H$alpha$ spectra
as test cases. Results. SunnyNet gives reasonable predictions for non-LTE
populations with a dramatic speedup of about 10$^5$ times when running on a
single GPU and compared to existing codes. When using different snapshots of
the same simulation for training and testing, SunnyNet’s predictions are within
20-40% of the true values for most points, which results in average differences
of a few percent in H$alpha$ spectra. Predicted H$alpha$ intensity maps agree
very well with existing codes. Most importantly, they show the telltale signs
of 3D radiative transfer in the morphology of chromospheric fibrils. The
results are not as reliable when the training and testing are done with
different families of simulations. SunnyNet is open source and publicly
available.
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 as the true
values. The network was then used to predict non-LTE populations for other 3D
simulations, and synthetic spectra were computed from its predicted non-LTE
populations. We used a six-level model atom of hydrogen and H$alpha$ spectra
as test cases. Results. SunnyNet gives reasonable predictions for non-LTE
populations with a dramatic speedup of about 10$^5$ times when running on a
single GPU and compared to existing codes. When using different snapshots of
the same simulation for training and testing, SunnyNet’s predictions are within
20-40% of the true values for most points, which results in average differences
of a few percent in H$alpha$ spectra. Predicted H$alpha$ intensity maps agree
very well with existing codes. Most importantly, they show the telltale signs
of 3D radiative transfer in the morphology of chromospheric fibrils. The
results are not as reliable when the training and testing are done with
different families of simulations. SunnyNet is open source and publicly
available.
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