Precovery and Impact Analysis Software Pipeline for Newly Discovered Potentially Hazardous Asteroids

Precovery provides a powerful complement to traditional follow-up observations by locating potential near-Earth object (NEO) detections in archival image data. This is achieved by using sky positions derived from back-propagated initial orbit estimates. Unlike conventional follow-up approaches, precovery can significantly reduce orbit uncertainties for new NEO candidates without requiring substantial new observing resources. However, the process remains challenged by the vast search spaces arising from high initial uncertainties and by the limited sensitivity of current sensors.

To overcome these challenges, we have developed an efficient synthetic tracking pipeline capable of detecting NEOs fainter than the single-frame sensitivity limit across large search areas. While traditional synthetic tracking requires computationally intensive shift-stacking of images along many trajectory hypotheses, our approach employs a dynamic-programming, divide-and-conquer algorithm that efficiently explores large trajectory spaces—enabling rapid, robust detection of faint moving objects. This method has already demonstrated substantial computational speedups, achieving detections of solar system objects orders of magnitude fainter than the single-frame threshold, even with limited prior state knowledge.

Building on this foundation, we are developing an end-to-end precovery and impact analysis framework that leverages the NASA Planetary Data System (PDS) Small Body Node (SBN). The framework will automate orbit back-propagation, uncertainty estimation, candidate image retrieval, detection analysis, and confidence quantification—establishing the first systematic, multi-dataset precovery search capability.

Supported by the UMD/APL ASTRA Grant