A Freeform Forward-modeling Pipeline for High-contrast Images of Circumstellar Disks Based on Automatic Differentiation

Modeling circumstellar disks in the traditional sense carries the assumption that the dust density distribution can be accurately described with a fixed parametric form. Furthermore, commonly used algorithms for subtracting the stellar point-spread function (PSF) distort the true morphology of the faint underlying disk structure, especially dusty features that are located at small angular separations. These phenomena often lead to significant residuals with parametric disk models and make it difficult to measure the full realizable range of the scattering function of the dust. We address these challenges with ffortissimo, a novel, pixel-based freeform forward modeling pipeline designed to characterize extended objects in images reduced using the Karhunen–Loéve Image Projection algorithm. We built this pipeline within the framework of Jax, which is a machine learning library in Python that enables efficient optimization through automatic differentiation (“autodiff”) and graphics processing unit–accelerated array computations. Using visible light images of the disk around HR 4796A taken by the “extreme” Magellan Adaptive Optics instrument (MagAO-X), we show that our data-driven freeform models excel at fitting a complex dust distribution and can infer the dust scattering properties even through PSF subtraction artifacts. Additionally, we demonstrate the potential for retrieving spatial dust features beyond the diffraction limit of the telescope. We note that there are remaining challenges to address before precision photometry using these freeform models is advised. These include a better background, wind-driven halo, and speckle characterization, as preventing the freeform models from learning these noise artifacts is currently difficult.