SPIN4D: Spectropolarimetric Inversion in 4-Dimensions

Xudong Sun1, Kai Yang1, Jiayi Liu1, Yannik Glaser1, Lucas Tarr2, Matthias Rempel3, Sarah Jaeggli2, Tom Schad2, S. C. Dodds1, Peter Sadowski1,
1University of Hawaii Manoa 2National Solar Observatory 3High Altitude Observatory
Paper Supplementary Code Data arXiv

Continuum emission at 500 nm from the SSD + 200 G case.

LOS Velocity of optical layer tau=1, of SSD + 200 G case.

LOS Magnetic Field of optical layer tau=1, of SSD + 200 G case.

Abstract

The National Science Foundation's Daniel K. Inouye Solar Telescope (DKIST) will provide high-resolution, multi-line spectropolarimetric observations that are poised to revolutionize our understanding of the Sun. Given the massive data volume, novel inference techniques are required to unlock its full potential. Here, we provide an overview of our "SPIn4D" project, which aims to develop deep convolutional neural networks (CNNs) for estimating the physical properties of the solar photosphere based on DKIST observations. We describe the magnetohydrodynamic (MHD) modeling and the Stokes profile synthesis pipeline that produce the simulated output and input data, respectively. These data will be used to train a set of CNNs that can rapidly infer the 4D MHD state vectors by exploiting the spatiotemporally coherent patterns in the Stokes-profile time series. Specifically, our radiative MHD model simulates the small-scale dynamo actions that are prevalent in quiet-Sun and plage regions. Four cases with different mean vertical magnetic fields have been conducted; each case covers six solar-hours, totaling 64 TB in data volume. The simulation domain covers 25x25x8 Mm with 16x16x12 km spatial resolution, extending from the upper convection zone up to the temperature minimum. The outputs are generated at a 40 s cadence. We forward model the Stokes profile of two sets of Fe1 lines at 630 and 1565 nm, which will be simultaneously observed by DKIST and can better constrain the parameter variations along the line of sight. The MHD model output and the synthetic Stokes profiles are publicly available.

Poster

BibTeX

@article{Yang_2024,
doi = {10.3847/1538-4357/ad865b},
url = {https://dx.doi.org/10.3847/1538-4357/ad865b},
year = {2024},
month = {nov},
publisher = {The American Astronomical Society},
volume = {976},
number = {2},
pages = {204},
author = {Kai E. Yang and Lucas A. Tarr and Matthias Rempel and S. Curt Dodds and Sarah A. Jaeggli and Peter Sadowski and Thomas A. Schad and Ian Cunnyngham and Jiayi Liu and Yannik Glaser and Xudong Sun},
title = {Spectropolarimetric Inversion in Four Dimensions with Deep Learning (SPIn4D). I. Overview, Magnetohydrodynamic Modeling, and Stokes Profile Synthesis},
journal = {The Astrophysical Journal},
abstract = {The National Science Foundation’s Daniel K. Inouye Solar Telescope (DKIST) will provide high-resolution, multiline spectropolarimetric observations that are poised to revolutionize our understanding of the Sun. Given the massive data volume, novel inference techniques are required to unlock its full potential. Here, we provide an overview of our “SPIn4D” project, which aims to develop deep convolutional neural networks (CNNs) for estimating the physical properties of the solar photosphere from DKIST spectropolarimetric observations. We describe the magnetohydrodynamic (MHD) modeling and the Stokes profile synthesis pipeline that produce the simulated output and input data, respectively. These data will be used to train a set of CNNs that can rapidly infer the four-dimensional MHD state vectors by exploiting the spatiotemporally coherent patterns in the Stokes profile time series. Specifically, our radiative MHD model simulates the small-scale dynamo actions that are prevalent in quiet-Sun and plage regions. Six cases with different mean magnetic fields have been explored; each case covers six solar-hours, totaling 109 TB in data volume. The simulation domain covers at least 25 × 25 × 8 Mm, with 16 × 16 × 12 km spatial resolution, extending from the upper convection zone up to the temperature minimum region. The outputs are stored at a 40 s cadence. We forward model the Stokes profile of two sets of Fe i lines at 630 and 1565 nm, which will be simultaneously observed by DKIST and can better constrain the parameter variations along the line of sight. The MHD model output and the synthetic Stokes profiles are publicly available, with 13.7 TB in the initial release.}
}