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|Web of Science®
|Globally optimal shape and spin pole determination with lightcurve inversion
|Monthly Notices of the Royal Astronomical Society, 2022; 513(1):311-332
|Oxford University Press (OUP)
|Chee-Kheng Chng, Michele Sasdelli, and Tat-Jun Chin
|Light-curve inversion is an established technique in determining the shape and spin states of an asteroid. However, the front part of the processing pipeline, which recovers the spin pole and area of each facet, is a non-convex optimization problem. Hence, an y local iterative optimization scheme can only promise a locally optimal solution. Apart from the obvious downsides of getting a non-optimal solution and the need for an initialization scheme, another major implication is that it creates an ambiguous scenario –which is to be blamed for the remaining residual? The inaccuracy of the modelling, the integrity of the data, or the non-global algorithm? We address the last uncertainty in this paper by embedding the spin pole and area vector determination module in a deterministic global optimization framework. To the best of our knowledge, this is the first attempt to solve these parameters globally . Specifically , given calibrated light-curve data, a scattering model for the object, and spin period, our method outputs the globally optimal spin pole and area vector solutions. One theoretical contribution of this paper is the introduction of a lower bound error function that is derived based on (1) the geometric relationship between the incident and scattered light on a surface and (2) the uncertainty of the gap between the observed and estimated brightness at a particular epoch in a light curve. We validated our method’s ability in achieving global minimum with both simulated and real light-curve data. We also tested our method on the real light curves of four asteroids.
|© 2022 The Author(s). Published by Oxford University Press on behalf of Royal Astronomical Society. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://cr eativecommons.or g/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
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|Australian Institute for Machine Learning publications
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