Constraining the shape of dark matter haloes with globular clusters and diffuse stellar light in the E-MOSAICS simulations
Bibliographic record
Abstract
ABSTRACT We explore how diffuse stellar light and globular clusters (GCs) can be used to trace the matter distribution of their host halo using an observational methodology. For this, we use 117 simulated dark matter (DM) haloes from the $(34.4~\rm cMpc)^3$ periodic volume of the E-MOSAICS project. For each halo, we compare the stellar surface brightness and GC projected number density maps to the surface density of DM. We find that the dominant structures identified in the stellar light and GCs correspond closely with those from the DM. Our method is unaffected by the presence of satellites and its precision improves with fainter GC samples. We recover tight relations between the dimensionless profiles of stellar-to-DM surface density and GC-to-DM surface density, suggesting that the profile of DM can be accurately recovered from the stars and GCs (σ ≤ 0.5 dex). We quantify the projected morphology of DM, stars, and GCs and find that the stars and GCs are more flattened than the DM. Additionally, the semimajor axes of the distribution of stars and GCs are typically misaligned by ∼10 degrees from that of DM. We demonstrate that deep imaging of diffuse stellar light and GCs can place constraints on the shape, profile, and orientation of their host halo. These results extend down to haloes with central galaxies M⋆ ≥ 1010 M⊙, and the analysis will be applicable to future data from the Euclid, Roman, and the Rubin observatories
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".