Abundance matching tested on small scales with galaxy dynamics
Bibliographic record
Abstract
Abstract We present a comprehensive test of the relation between stellar and total masses in galaxies as predicted by popular models based on abundance matching (AM) techniques. We use the ‘Spectroscopy and H-band Imaging of Virgo cluster galaxies’ (SHIVir) survey with photometric and dynamical profiles for 190 Virgo cluster galaxies to establish a relation between the stellar and dynamical masses measured within the isophotal radius r23.5. Various dark matter and galaxy scaling relations are combined with results from the NIHAO (Numerical Investigation of Hundred Astrophysical Objects) suite of hydrodynamical simulations to recast AM predictions in terms of these observed quantities. Our results are quite insensitive to the exact choice of dark matter profile and halo response to baryon collapse. We find that theoretical models reproduce the slope and normalization of the observed stellar-to-halo mass relation (SHMR) over more than three orders of magnitude in stellar mass (108 < M*/M⊙ < 2 × 1011). However, the scatter of the observed SHMR exceeds that of AM predictions by a factor of ∼5. For systems with stellar masses exceeding 5 × 1010 M⊙, AM overpredicts the observed stellar masses for a given dynamical mass. The latter offset may support previous indications of a different stellar initial mass function in these massive galaxies. Overall, our results support the validity of AM predictions on a wide dynamical range.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".