A Near-Earth Object Survey Planning and Scheduling Model for Multi-Application Survey Telescope Array

It has become a consensus that near-Earth asteroids (NEAs) threaten global security, thus their detection is of great significance. The Multi-Application Survey Telescope Array (MASTA), with its wide field of view, multiple telescopes, and large aperture, is particularly well suited for NEA detection. Its addition can significantly enhance China's capability in this regard. To leverage MASTA's advantages in NEA detection, we propose a survey strategy for near-Earth objects and present a scheduling model based on the integer linear programming method. To evaluate the model's effectiveness and MASTA's performance in NEA detection under this framework, we generate a synthetic NEA dataset by augmenting the known population and then conduct a one-year survey simulation. The simulation results indicate that the scheduling model can effectively meet the requirements of MASTA's survey for NEAs and optimize the allocation of observational resources while considering other scientific goals. We find that MASTA can achieve a one-year detection completeness of 1.29% for the synthetic NEA population.