Deriving the strength and direction of the three-dimensional (3D) magnetic
field in the solar atmosphere is fundamental for understanding its dynamics.
Volume information on the magnetic field mostly relies on coupling 3D
reconstruction methods with photospheric and/or chromospheric surface vector
magnetic fields. Infrared coronal polarimetry could provide additional
information to better constrain magnetic field reconstructions. However,
combining such data with reconstruction methods is challenging, e.g., because
of the optical-thinness of the solar corona and the lack and limitations of
stereoscopic polarimetry. To address these issues, we introduce the
Data-Optimized Coronal Field Model (DOCFM) framework, a model-data fitting
approach that combines a parametrized 3D generative model, e.g., a magnetic
field extrapolation or a magnetohydrodynamic model, with forward modeling of
coronal data. We test it with a parametrized flux rope insertion method and
infrared coronal polarimetry where synthetic observations are created from a
known "ground truth" physical state. We show that this framework allows us to
accurately retrieve the ground truth 3D magnetic field of a set of force-free
field solutions from the flux rope insertion method. In observational studies,
the DOCFM will provide a means to force the solutions derived with different
reconstruction methods to satisfy additional, common, coronal constraints. The
DOCFM framework therefore opens new perspectives for the exploitation of
coronal polarimetry in magnetic field reconstructions and for developing new
techniques to more reliably infer the 3D magnetic fields that trigger solar
flares and coronal mass ejections.Comment: 14 pages, 6 figures; Accepted for publication in Ap