Mid-Atlantic Section Meeting 2021
Volume 66, Number 18
Friday–Sunday, December 3–5, 2021;
Rutgers University, New Brunswick, New Jersey
Session D02: Solar Corona
11:15 AM–1:03 PM,
Saturday, December 4, 2021
Room: 201B
Chair: Sijie Yu, New Jersey Institute of Technology
Abstract: D02.00001 : An automated framework for 3D data-constrained modeling of the coronal magneto-thermal structure of solar active regions*
11:15 AM–11:51 AM
Preview Abstract
Abstract
Author:
Gelu Nita
(New Jersey Inst of Tech)
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. GX
Simulator is a publicly available data-constrained 3D modeling package
distributed through the SolarSoftWare (SSW) IDL repository, which has been
developed for modeling multiwavelength emission in the microwave, X-ray, and
EUV ranges from flaring loops (Nita et al. 2015, ApJ 799, 236) and solar
active regions (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 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 Imager
(HMI) onboard the Solar Dynamics Observatory (SDO) and, optionally, the
contextual Atmospheric Imaging Assembly (AIA) maps, performs potential
and/or nonlinear force free field extrapolations, populates the volume with
thermal 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, but the iterative search for the
best model parameters for agreement between the model and observations is a
time-consuming task that calls for a more efficient, automated approach. To
this end, 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 synthetic maps, which are automatically compared
with the reference observational data until the desired level of agreement
is achieved, as measured by objective data-to-model comparison metrics. In
this presentation, I will 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
*This work was partly supported by the NSF AST-1820613 and AGS-1743321 grants to the New Jersey Institute of Technology