Finding Asteroids Down the Back of the Couch: A Novel Approach to the Minor Planet Linking Problem

TL;DR

This paper introduces a scalable heliocentric transformation algorithm for linking minor planet tracklets, significantly improving efficiency and enabling orbit determination for thousands of new asteroids from survey data.

Contribution

The authors develop a novel $O(N \, log N)$ clustering algorithm for minor planet tracklets, overcoming computational bottlenecks of previous methods.

Findings

Successfully established orbits for approximately 41,000 new minor planets.

Demonstrated the algorithm's scalability and efficiency on real survey data.

Outperformed standard methods with a significant reduction in computational complexity.

Abstract

We present a novel approach to the minor planet linking problem. Our heliocentric transformation-and-propagation algorithm clusters tracklets at common epochs, allowing for the efficient identification of tracklets that represent the same minor planet. This algorithm scales as $O(N log N)$, with the number of tracklets $N$, a significant advance over standard methods, which scale as $O(N^3)$. This overcomes one of the primary computational bottlenecks faced by current and future asteroid surveys. We apply our algorithm to the Minor Planet Center's Isolated Tracklet File, establishing orbits for $\sim41,000$ new minor planets.