The asteroid belt is a unique source of information on some of the most important questions facing solar system science. These questions include the sizes, numbers, types and orbital distributions of the planetesimals that formed the planets, and the identification of those asteroids that are the sources of meteorites and near-Earth asteroids. Answering these questions requires an understanding of the dynamical evolution of the asteroid belt, but this evolution is governed by a complex interplay of mechanisms that include catastrophic disruption, orbital evolution driven by Yarkovsky radiation forces, and chaotic orbital evolution driven by gravitational forces. While the timescales of these loss mechanisms have been calculated using estimates of some critical parameters that include the thermal properties, strengths and mean densities of the asteroids, we argue here that the uncertainties in these parameters are so large that deconvolution of the structure of the asteroid belt must be guided primarily by observational constraints. We argue that observations of the inner asteroid belt indicate that the size-frequency distribution is not close to the equilibrium distribution postulated by Dohnanyi (1969). We also discuss the correlations observed between the sizes and the orbital elements of the asteroids. While some of these correlations are significant and informative, others are spurious and may arise from the limitations of the Hierarchical Clustering Method that is currently used to define family membership.

Studies of white dwarfs have greatly benefited from time-domain surveys and subsequent follow-up observations. However, with the avalanche of alerts delivered by ZTF and LSST and the limited resources for follow-up, we will need brokers to select intriguing alerts that warrant follow-up in a timely manner. At the University of Arizona and NSF’s OIR Lab, we are developing the Arizona-NOAO Temporal Analysis and Response to Events System, to hunt for the rarest of the rare events in the time-domain. In this work, we provide an overview of the ANTARES system, how we use ZTF as a training set, and the way forwards to LSST.