Critical Early DKIST Science: Spectropolarimetric Inversion in Four Dimensions with Deep Learning

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The solar photosphere is an amazingly complex system of interacting plasma structures, each with its own complex magnetic field. In order to determine their physical parameters, one must solve a complicated “inversion” problem. Current methods are so slow as to be incapable of keeping up with the volume of data to be collected by NSF’s Daniel K. Inouye Solar Telescope (DKIST). This work will pioneer the use of Deep Learning, in effect “teaching” a supercomputer to determine physical parameters of the solar photosphere given data from DKIST. This new tool will also advance the field of machine learning by adapting the technique to a complex astrophysical phenomenon. A post-doctoral researcher and a graduate student at the University of Hawaii will both be trained in these novel methods as a part of the work. The team will create spectropolarimetric data and Stokes parameters for simulated photospheric structures by modeling these structures using the MURaM MHD code. These simulations will be used to ‘train’ a computer program to identify state parameters using open-source Deep Learning (DL) models, or convolutional neural networks. The DL system will then be validated through comparison to SIR inversion codes. The team will explore the domain adaptation methods to reduce differences between the simulation and observation. In the final phase of the work, the Deep Learning system will then be tested and refined using time sequences of multi-line Stokes data from DKIST’s DL-NIRSP instrument, due to start operations as a “first light” instrument on DKIST.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.