Consequences of asteroid characterization on the state of knowledge about inferred physical parameters and impact risk

Abstract

Planetary defense scenarios require an assessment of the possible effects of an impact and of the efficacy of different mitigation options in order to provide decision makers with actionable information. Essential inputs to these assessments are the orbital parameters, the likelihood of impact, the possible locations of impact, and the physical properties of the potential impactor. There is a robust framework in place to determine the orbital parameters, the likelihood of impact, and the possible locations of impact (e.g. Milani et al. (2002), Sciarra et al. (2020), Losacco et al. (2018)). While there is a robust suite of potential methods to determine properties of a potential impactor, most of these methods depend critically on the details of the objects observability (e.g. brightness, distance from Earth, …). As a result, in practice it is rare that all the physical properties of interest are measureable. In order to fill this gap, we have developed a property inference network that can be used to generate a set of potential impactors whose properties in aggregate are consistent with the distributions from the available measurements, the distributions derived from population-wide statistics, and the combination of physical properties for each individual impactor is physically plausible. We will describe this Asteroid Property Inference Network (APIN), apply it to the PDC23 scenario, and demonstrate how even limited asteroid characterization can be leveraged to improve impact risk assessment.