On the tidal formation of dark matter-deficient galaxies
Bibliographic record
Abstract
ABSTRACT Previous studies have shown that dark matter-deficient galaxies (DMDG) such as NGC 1052-DF2 (hereafter DF2) can result from tidal stripping. An important question, though, is whether such a stripping scenario can explain DF2’s large specific frequency of globular clusters (GCs). After all, tidal stripping and shocking preferentially remove matter from the outskirts. We examine this using idealized, high-resolution simulations of a regular dark matter-dominated galaxy that is accreted on to a massive halo. As long as the initial (pre-infall) dark matter halo of the satellite is cored, which is consistent with predictions of cosmological, hydrodynamical simulations, the tidal remnant can be made to resemble DF2 in all its properties, including its GC population. The required orbit has a pericentre at the 8.3 percentile of the distribution for subhaloes at infall, and thus is not particularly extreme. On this orbit the satellite loses 98.5 (30) per cent of its original dark matter (stellar) mass, and thus evolves into a DMDG. The fraction of GCs that is stripped off depends on the initial radial distribution. If, at infall, the median projected radius of the GC population is roughly two times that of the stars, consistent with observations of isolated galaxies, only ∼20 per cent of the GCs are stripped off. This is less than for the stars, which is due to dynamical friction counteracting the tidal stirring. We predict that, if indeed DF2 was crafted by strong tides, its stellar outskirts should have a very shallow metallicity gradient.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".