AUTHOR=Li Fan , Yuan Ye , Fu Yanning , Chen Jian TITLE=Simulation study of asteroid mass determination using CSST asteroid observations JOURNAL=Frontiers in Astronomy and Space Sciences VOLUME=10 YEAR=2023 URL=https://www.frontiersin.org/journals/astronomy-and-space-sciences/articles/10.3389/fspas.2023.1230666 DOI=10.3389/fspas.2023.1230666 ISSN=2296-987X ABSTRACT=

The objective of this study is to explore the potential of the Chinese Space Station Telescope (CSST) in asteroid mass determination with asteroid-asteroid close encounters. The CSST is expected to observe some asteroids with an accuracy of several milliarcseconds and has a limiting magnitude of 26 (AB mag) or higher in the g and r bands. By combining CSST observations with existing ground-based observations, significant improvements in asteroid mass precision can be achieved. To quantify the CSST’s capability in asteroid mass determination, three types of simulations are conducted. In Type A simulation, 58 close encounters with available Gaia DR2 observations were considered, assuming CSST observes asteroids at a frequency similar to Gaia’s. After using the simulated CSST observations, asteroid mass precision is improved substantially. In seven events, the determined precision are better than 5%. Type B simulation is performed based on a tentative optical survey plan of CSST, but the limited opportunities to observe asteroids involved in a close encounter with strong perturbation from to-be-determined masses. As a result, the precision of mass determination is low, though the improvement brought by CSST data is obvious. This implies that the dedicated observations are necessary for CSST to contribute masses with high precision. Type C simulation is performed with a small amount of CSST observing time, to be specific for a strong encounter, 144 observations spanning 3 years centered at the encounter time, totaling about 7.2 observation hours. In this case, CSST can determine a number of asteroid masses, of which 10 asteroid’s precision are expected to be better than 10%.