Measuring Apsidal Clustering
Amir Siraj, Christopher F. Chyba, Scott Tremaine
TLDR
This paper introduces a new conditional-likelihood method to measure apsidal clustering in the outer solar system, finding reduced significance for the phenomenon.
Key contributions
- Developed a new conditional-likelihood method for measuring apsidal clustering, robust to uneven survey footprints.
- Expanded the sample of relevant distant TNOs from 21 to 25 by calculating their long-term orbital stability.
- Applied the new method, reducing the significance of apsidal clustering from 2.7σ to 1.9σ.
- Demonstrated that the direction of any potential apsidal clustering is currently not well constrained.
Why it matters
The debate over apsidal clustering and the existence of Planet X is ongoing. This new, robust method provides a more accurate way to assess the evidence. Its application to future data from LSST will be crucial in resolving this long-standing astronomical mystery.
Original Abstract
The decade-long debate over the existence of apsidal clustering in the outer solar system is poised for reignition given the plethora of distant trans-Neptunian object (TNO) discoveries expected from the forthcoming Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST). Here, we present a new conditional-likelihood method to measure apsidal clustering that is insensitive to uneven survey footprints. We calculate the long-term orbital stability of distant TNOs, which allows us to expand the known sample of relevant objects from 21 to 25. We apply our new method to this up-to-date sample, showing that the significance of the apsidal clustering in the outer solar system has fallen from $2.7σ$ to $1.9σ$, and that the direction of clustering is not well constrained. This new method is suitable for application to the growing sample of known TNOs, and the results will reveal whether the evidence for a hypothetical Planet X from apsidal clustering is real or spurious.
📬 Weekly AI Paper Digest
Get the top 10 AI/ML arXiv papers from the week — summarized, scored, and delivered to your inbox every Monday.