Unraveling the Complexity of Dwarf Galaxy Dynamics: A Study of Binary Orbital Motions
Bibliographic record
Abstract
Abstract We investigate the impact of binary orbital motions on the dynamical modeling of dwarf galaxies with intrinsic line-of-sight velocity dispersions ( σ v r ) of 1–9 km s −1 . Using dwarf galaxies from the auriga level-2 and level-3 simulations, we apply the Jeans Anisotropic Multi-Gaussian Expansion modeling to tracer stars before and after including binaries to recover the dynamical masses. The recovered total masses within the half-mass radius of tracers, M (< r half ), are always inflated due to binary motions, with greater inflations occurring for smaller σ v r . However, many dwarf galaxies experience central density deflated due to binary motions, with little dependence on σ v r . This is due to the negative radial gradients in the velocity dispersion profiles, with the fractional inflation in σ v r due to binaries more significant in outskirts. An extreme binary fraction of 70% can lead to central density deflation of up to 10%–20% at 3 km s −1 < σ v r < 8 km s −1 , with M ( < r half ) inflated by 4% at 9 km s −1 and up to 15% at 3 km s −1 . A lower binary fraction of 36% leads to similar deflations, with the inflations decreasing to approximately 10% at 3 km s −1 and becoming statistically insignificant. The choice of binary orbit distribution models does not result in significant differences, and observational errors tend to slightly weaken the deflations in the recovered central density. Two observations separated by 1 yr to exclude binaries lead to almost zero inflations/deflations for a binary fraction of 36% over 3 km s −1 < σ v r < 9 km s −1 . For σ v r
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".