Investigating gravitational collapse of a pebble cloud to form transneptunian binaries
Bibliographic record
Abstract
Context. A large fraction of transneptunian objects are found in binary pairs, ~30% in the cold classical population between a hel ~ 39 and ~48 AU. Observationally, these binaries generally have components of similar size and colour. Previous work has shown that gravitational collapse of a pebble cloud is an efficient mechanism for producing such systems. Since the bi-lobate nature of 2014 MU 69 (Arrokoth) was discovered, interest in gravitational collapse as a pathway for forming contact binaries has also grown. Aims. We investigate the formation of binary systems through gravitational collapse by considering a wider range of binary masses than previous studies. We analysed in detail the properties of the bound systems that are formed and compared them to observations. Methods. We performed N -body simulations of gravitational collapse of a pebble cloud using the REBOUND package, with an integrator designed for rotating reference frames and robust collision detection. We conducted a deep search for gravitationally bound particles at the end of the gravitational collapse phase and tested their stability. For all systems produced, not just for the most massive binaries, we investigated the population characteristics of their mass and orbital parameters. Results. We found that gravitational collapse is an efficient producer of bound planetesimal systems. On average, about 1.5 bound systems were produced per cloud in the mass range studied here. In addition to large equal-sized binaries, we found that gravitational collapse produces massive bodies with small satellites and low-mass binaries with a high mass ratio. Our results disfavour the collapse of high-mass clouds, in line with reported upper mass limits of clouds formed by the streaming instability. Gravitational collapse can create binary systems analogous to Arrokoth, and collisions in a collapsing cloud should be gentle enough to preserve a bi-lobed structure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".