ADAM: a general method for using various data types in asteroid reconstruction

TL;DR

ADAM is a versatile asteroid modeling algorithm that integrates various data types using Fourier transforms, enabling efficient and unified shape reconstruction from diverse observational data, including raw ALMA data.

Contribution

The paper introduces ADAM, a universal and efficient method for asteroid shape modeling that handles multiple data types uniformly via Fourier transforms, including raw ALMA data.

Findings

ADAM effectively combines different observational data types for asteroid modeling.

The method achieves fast convergence in shape reconstruction.

It reliably incorporates raw ALMA data without standard image construction.

Abstract

We introduce ADAM, the All-Data Asteroid Modelling algorithm. ADAM is simple and universal since it handles all disk-resolved data types (adaptive optics or other images, interferometry, and range-Doppler radar data) in a uniform manner via the 2D Fourier transform, enabling fast convergence in model optimization. The resolved data can be combined with disk-integrated data (photometry). In the reconstruction process, the difference between each data type is only a few code lines defining the particular generalized projection from 3D onto a 2D image plane. Occultation timings can be included as sparse silhouettes, and thermal infrared data are efficiently handled with an approximate algorithm that is sufficient in practice due to the dominance of the high-contrast (boundary) pixels over the low-contrast (interior) ones. This is of particular importance to the raw ALMA data that can be directly handled by ADAM without having to construct the standard image. We study the reliability of the inversion by using the independent shape supports of function series and control-point surfaces. When other data are lacking, one can carry out fast nonconvex lightcurve-only inversion, but any shape models resulting from it should only be taken as illustrative global-scale ones.