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Details

Autor(en) / Beteiligte
Titel
OSSOS. XIII. Fossilized Resonant Dropouts Tentatively Confirm Neptune's Migration Was Grainy and Slow
Ist Teil von
  • The Astronomical journal, 2019-06, Vol.157 (6), p.253
Ort / Verlag
Madison: The American Astronomical Society
Erscheinungsjahr
2019
Link zum Volltext
Quelle
Electronic Journals Library - Freely accessible e-journals
Beschreibungen/Notizen
  • The migration of Neptune's resonances through the proto-Kuiper Belt has been imprinted in the distribution of small bodies in the outer solar system. Here we analyze five published Neptune migration models in detail, focusing on the high pericenter distance (high-q) trans-Neptunian objects (TNOs) near Neptune's 5:2 and 3:1 mean-motion resonances because they have large resonant populations, are outside the main classical belt, and are relatively isolated from other strong resonances. We compare the observationally biased output from these dynamical models with the detected TNOs from the Outer Solar System Origins Survey (OSSOS) via its Survey Simulator. All four of the new OSSOS detections of high-q nonresonant TNOs are on the sunward side of the 5:2 and 3:1 resonances. We show that even after accounting for observation biases, this asymmetric distribution cannot be drawn from a uniform distribution of TNOs at 2 confidence. As shown by previous work, our analysis here tentatively confirms that the dynamical model that uses grainy slow Neptune migration provides the best match to the real high-q TNO orbital data. However, due to extreme observational biases, we have very few high-q TNO discoveries with which to statistically constrain the models. Thus, this analysis provides a framework for future comparison between the output from detailed, dynamically classified Neptune migration simulations and the TNO discoveries from future well-characterized surveys. We show that a deeper survey (to a limiting r-magnitude of 26.0) with a similar survey area to OSSOS could statistically distinguish between these five Neptune migration models.

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