A genetic algorithm approach to fitting interferometric data of post-AGB objects: I. the case of the Ant nebula
D. Macdonald, Orsola De Marco, Eric Lagadec, Jun Ma, O. Chesneau

TL;DR
This paper introduces GADRAD, a Python-based genetic algorithm tool for modeling post-AGB objects from interferometric data, providing detailed parameter estimation and insights into nebula shaping processes.
Contribution
GADRAD is the first tool to systematically use genetic algorithms for fitting interferometric data of post-AGB environments, revealing parameter correlations and degeneracies.
Findings
GADRAD accurately recovers parameters from simulated data.
Application to the Ant nebula yields a large dusty disc with a higher dust mass.
Parameter confidence intervals help constrain disc and system properties.
Abstract
We present GADRAD, a Python module that adopts heuristic search techniques in the form of genetic algorithms, to efficiently model post-asymptotic giant branch (post- AGB) disc environments. GADRAD systematically constructs the multi-dimensional pa- parameter probability density functions that arise from the fitting of radiative transfer and geometric models to optical interferometric data products. The result provides unbiased descriptions of the object's potential morphology, component luminosities and temperatures, dust composition, disc density profiles and mass. Correlation in the estimated parameters as well as potential degeneracies are revealed. Estimated probability distributions of the post-AGB environment parameters provide insight into the shaping processes that may occur in the transition from the post-AGB to the planetary nebula phase. We test parameter recovery on…
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Taxonomy
TopicsStellar, planetary, and galactic studies · Astronomy and Astrophysical Research · Astrophysics and Star Formation Studies
