The Near-Earth Object Surveyor (NEO Surveyor) will survey the sky in infrared wavelengths to detect and characterize Near-Earth Objects, including comets . While the number of known Near-Earth Comets is substantially smaller than the number of known Near-Earth Asteroids, these icy bodies still present a significant impact hazard to the Earth. Although the activity of comets is notoriously difficult to predict for individual objects due to the possibility of outbursting and seasonal events, the behavior of comets as an ensemble population is a somewhat more tractable problem.
The currently running surveys (e.g, PANSTARRS , Zwicky Transient Facility , ATLAS , WISE/NEOWISE ) are providing an excellent base on which the next-generation wide field surveys (e.g., NEO Surveyor  and the Vera C. Rubin Observatory ) can be built. Predictions of cometary behavior in our models include nucleus size, dust production, and gas production. These have been folded into the Reference Small Body Population Model (RSBPM) that is being developed by the NEO Surveyor team in order to verify and validate the performance of future survey missions and allow for debiasing of the observed comet populations . By modeling the expected brightness of the comet population, we can predict how many comets we expect to see with NEO Surveyor, and can optimize their detectability. Once the survey begins, we will then compare these predictions to the actual measurements to calculate the efficiency of the survey, and thus de-bias the survey to properly characterize the comet population. We will present the current status of our modeling work, including models of the distribution of several fundamental cometary parameters (e.g., nucleus size and albedo).
Acknowledgements: NEO Surveyor is a joint project of the University of Arizona and NASA’s Jet Propulsion Laboratory, sponsored by NASA’s Planetary Defense Coordination Office, a division of NASA’s Planetary Science Directorate.
References:  Mainzer et al., 2015, AJ 149:5, article id 172;  Denneau et al., 2013, PASP, 125:926;  Masci et al., 2019, PASP, 131:995;  Tonry et al., 2018, PASP, 130:988;  Mainzer et al., 2011, ApJ, 731:53;  Grav, Mainzer, and Spahr, 2016, AJ, 151:6.