Modern sky surveys provide an unprecedented volume of photometric and astrometric data that can be exploited to study the physical properties of asteroids on a population scale. In this work, it is demonstrated how multi-band observations from the Sloan Digital Sky Survey (SDSS) and SkyMapper are utilized to extract detections and probabilistically infer taxonomic classes for hundreds of thousands of asteroids, far surpassing the limits of traditional spectroscopic studies. These large-scale photometric datasets are complemented by statistical analyses of space-based reflectance spectra, such as those from Gaia, in which machine-learning techniques are applied to refine compositional constraints across dynamical populations. Furthermore, it is shown that survey light curves, incorporating both dense and sparse time-series data from missions like Kepler, TESS, and ZTF, enable the determination of rotational periods, shapes, and albedos. These results highlight sky surveys as a powerful and efficient tool for advancing the understanding of the physical diversity and evolutionary history of small bodies in the Solar System.
https://cnrs.zoom.us/j/98214587551?pwd=ES90wI8wRhMQ1htcq8pnxGRa44nWau.1