REPRODUCING THE STELLAR MASS/HALO MASS RELATION IN SIMULATED ΛCDM GALAXIES: THEORY VERSUS OBSERVATIONAL ESTIMATES
Bibliographic record
Abstract
We examine the present-day total stellar-to-halo mass (SHM) ratio as a function of halo mass for a new sample of simulated field galaxies using fully cosmological, ΛCDM, high-resolution SPH + N -body simulations. These simulations include an explicit treatment of metal line cooling, dust and self-shielding, H 2 -based star formation (SF), and supernova-driven gas outflows. The 18 simulated halos have masses ranging from a few times 10 8 to nearly 10 12 M ☉ . At z = 0, our simulated galaxies have a baryon content and morphology typical of field galaxies. Over a stellar mass range of 2.2 × 10 3 –4.5 × 10 10 M ☉ we find extremely good agreement between the SHM ratio in simulations and the present-day predictions from the statistical abundance matching technique presented in Moster et al. This improvement over past simulations is due to a number systematic factors, each decreasing the SHM ratios: (1) gas outflows that reduce the overall SF efficiency but allow for the formation of a cold gas component; (2) estimating the stellar masses of simulated galaxies using artificial observations and photometric techniques similar to those used in observations; and (3) accounting for a systematic, up to 30% overestimate in total halo masses in DM-only simulations, due to the neglect of baryon loss over cosmic times. Our analysis suggests that stellar mass estimates based on photometric magnitudes can underestimate the contribution of old stellar populations to the total stellar mass, leading to stellar mass errors of up to 50% for individual galaxies. These results highlight that implementing a realistic high density threshold for SF considerably reduces the overall SF efficiency due to more effective feedback. However, we show that in order to reduce the perceived tension between the SF efficiency in galaxy formation models and in real galaxies, it is very important to use proper techniques to compare simulations with observations.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".