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An automated framework for generating parametrized 3D data-constrained models of the coronal magneto-thermal structure of solar active regions

Presentation #122.02 in the session Coronal Heating: Present Understanding and Future Progress — Poster Session.

Published onOct 20, 2022
An automated framework for generating parametrized 3D data-constrained models of the coronal magneto-thermal structure of solar active regions

Data-constrained modeling of the coupling between the magnetic and thermal structures of solar active regions (ARs) is a crucial step towards understanding the source region of flares and coronal mass ejections, which may cause adverse effects on the near-Earth environment. GX Simulator is a publicly available data-constrained 3D modeling package distributed through the SolarSoftWare (SSW) IDL repository, which has been developed for the purpose of modeling multiwavelength emission in the microwave, X-ray, and EUV ranges from flaring loops (Nita et al. 2015, ApJ 799, 236) and ARs (Nita et al. 2018, ApJ 853, 66). To facilitate its use, a fully automatic GX Simulator-compatible model production pipeline (AMPP) has been developed. Based on minimal user’s input provided as a script or through an intuitive graphical user interface (GUI), the AMPP downloads the required vector magnetic field data produced by the Helioseismic and Magnetic lmager (HMI) onboard the Solar Dynamics Observatory (SDO) and , optionally, the contextual Atmospheric Imaging Assembly (AIA) maps, performs potential and/or nonlinear force free field (NLFFF) extrapolations, populates the magnetic field skeleton with parametrized heated plasma coronal models that assume either steady-state or impulsive plasma heating, and generates non-LTE density and temperature distribution models of the chromosphere that are constrained by photosphere-level measurements. The standardized models produced by AMPP may be further customized through a set of GX Simulator interactive GUI tools, to systematically search for, and validate, the combination of adjustable model parameters providing the best possible agreement between the synthetic and observational maps. This is a computationally intense iterative process that requires an efficient, automated approach. For this purpose, we have developed a coronal heating modeling pipeline (CHMP), which is a fully automated multi-threaded search engine that adaptively steps through a multi-dimensional parameter space, to produce parametrized test models and generate corresponding synthetic maps, which are compared with the reference observational data until the desired level of agreement, measured by objective data-to-model comparison metrics, is achieved. ln this presentation, we describe the architecture of the AMPP and CHMP components of the GX Simulator package and demonstrate their functionality in the case of a particular solar active region.

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