arXiv subjects

Avalon Gower

Publications and source records attributed to Avalon Gower.

3 recordsLinked to original sources

arXiv2026

windsoCC: reconstructing the wind-driven halo in MagAO-X images using wavefront sensor telemetry

The wind-driven halo (WDH) is a persistent, low spatial frequency noise artifact that arises due to the servo-lag error inherent to all adaptive optics (AO) instruments. Spatial filtering may be employed to overcome this artifact, however, filtering out the WDH while simultaneously preserving signal from an extended astrophysical object of interest is exceptionally challenging. Additionally, since the WDH changes in intensity and position angle through an observation, data-driven algorithms (e.g., KLIP) that are commonly used to subtract the starlight need to be overly-aggressive to remove both the static and dynamic noise components. Since wavefront sensors (WFSs) continuously track the closed-loop residual wavefront error, WFS telemetry presents the ideal resource for combating this type of noise artifact through postprocessing. Using archival WFS telemetry from MagAO-X, which is the ``extreme" AO instrument for the 6.5-meter Magellan-Clay telescope, we demonstrate a novel workflow for WDH reconstruction and removal in individual coronagraphic science images. MagAO-X is equipped with a pyramid WFS capable of recording wavefront telemetry at a high-cadence which is saved during data acquisition. Given this, we detail how our WFS data processing pipeline, windsoCC, cross-correlates the recorded closed-loop wavefront to measure the wind vectors of several turbulent layers of the atmosphere above Las Campanas Observatory. We then make use of the wind parameters learned through windsoCC to reconstruct the WDH footprint by leveraging a parametric model. Notably, we demonstrate a dramatic improvement in object recovery using on-sky MagAO-X images of the disk around HR~4796A at visible wavelengths.

astro-ph.IM↗

arXiv2026

ffortissimo: 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 KLIP-reduced images. 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 GPU-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 better background, wind-driven halo, and speckle characterization as preventing the freeform models from learning these noise artifacts is currently difficult.

astro-ph.IM↗

arXiv2026

A Multiband Study of the HR 4796A Disk in the Optical Using MagAO-X

We present total intensity images of the debris disk around HR 4796A from observations spanning 2023 to 2025 with the Magellan extreme adaptive optics instrument (MagAO-X). We detected the disk at high signal-to-noise ratios at $g' (527$ nm), $r' (615$ nm), $i' (762$ nm), and $z' (909 $ nm). Additionally, we present images collected using the "star-hopping" technique that show the entirety of the disk, including the dramatic forward-scattering at the minor axis. We subjected our images to a battery of modeling techniques to constrain the geometry and photometry of the disk. Leveraging our clear detections of the disk's minor axis, we modeled the scattering phase function (SPF) using a basis of the Legendre polynomials. To mitigate self-subtraction artifacts in our angular differential imaging, we implemented a forward-modeling pipeline that generates a pixel-based freeform disk forward model leading to a deconvolved image of the disk. Our best-fit disk models reveal: (1) highly forward-scattering SPFs with a minimum at the $\sim65^{\circ}$ scattering angle, (2) a faint halo of dust just exterior to the spine of the disk that is not well-described by a broken power law density profile, (3) a red spectral slope for the dust, and finally (4) a compact, clump-like feature in the freeform disk models. Our empirically-measured SPFs suggest that the scattering is dominated by large, highly-absorptive grains. However, we emphasize the need for testing advanced irregular grain models using our SPFs to learn more about the physical and chemical properties of this complex system.

astro-ph.EP↗