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Causal evidence for the primordiality of colours in trans-Neptunian objects
Davis, Benjamin L. ; Ali-Dib, Mohamad ; Zheng, Yujia ; Jin, Zehao ; Zhang, Kun ; Maccio, Andrea V.
Davis, Benjamin L.
Ali-Dib, Mohamad
Zheng, Yujia
Jin, Zehao
Zhang, Kun
Maccio, Andrea V.
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slaf089.pdf
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Department
Machine Learning
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Journal article
Date
2025
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Language
English
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Abstract
The origins of the colours of trans-Neptunian objects (TNOs) represent a crucial unresolved question, central to understanding the history of our Solar system. Recent observational surveys revealed correlations between the eccentricity and inclination of TNOs, and their colours. This rekindled the long-standing debate on whether these colours reflect the conditions of TNO formation or their subsequent evolution. We address this question using a model-agnostic, data-driven approach that unanimously converges to a common causal graph from the analysis of two different data sets, each from two different conditional independence test methods. For evaluation, we demonstrate how our model is consistent with the currently-accepted paradigms of TNOs' dynamical histories, without involving any orbital modelling or physics-based assumptions. Our causal model (with no knowledge of the existence of Neptune) predicts the need for an unknown confounding variable, consistent with Neptune's effects. The model predicts that the colour of TNOs is the root cause of their inclination distribution, rather than the other way around. This strongly suggests that the colours of TNOs reflect an underlying dynamical property, most likely their formation location. Our model excludes formation scenarios that invoke substantial colour modification by subsequent evolution. We conclude that the colours of TNOs are predominantly primordial.
Citation
B. L. Davis, M. Ali-Dib, Y. Zheng, Z. Jin, K. Zhang, and A. V. Macciò, “Causal evidence for the primordiality of colours in trans-Neptunian objects,” Mon Not R Astron Soc Lett, vol. 543, no. 1, pp. L34–L42, Aug. 2025, doi: 10.1093/MNRASL/SLAF089
Source
Monthly Notices of the Royal Astronomical Society: Letters
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Keywords
Kuiper Belt: General, Methods: Statistical, Software: Machine Learning
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Oxford University Press
