Effects of baryonic and dark matter substructure on the Pal 5 stream
Bibliographic record
Abstract
Gravitational encounters between small-scale dark matter substructure and cold stellar streams in the Milky Way halo lead to density perturbations in the latter, making streams an effective probe for detecting dark matter substructure. The Pal 5 stream is one such system for which we have some of the best data. However, Pal 5 orbits close to the centre of the Milky Way and has passed through the Galactic disc many times, where its structure can be perturbed by baryonic structures such as the Galactic bar and giant molecular clouds (GMCs). In order to understand how these baryonic structures affect Pal 5’s density, we present a detailed study of the effects of the Galactic bar, spiral structure, GMCs, and globular clusters on the Pal 5 stream. We estimate the effect of each perturber on the stream density by computing its power spectrum and comparing it to the power induced by a CDM-like population of dark matter subhaloes. We find that the bar and GMCs can each individually create power that is comparable to the observed power on large scales, leaving little room for dark matter substructure, while spirals are subdominant on all scales. On degree scales, the power induced by the bar is small, but GMCs create small-scale density variations that are similar in amplitude to the dark-matter induced variations but otherwise indistinguishable from it. These results demonstrate that Pal 5 is a poor system for constraining the dark matter substructure fraction and that observing streams further out in the halo will be necessary to confidently detect dark matter subhaloes.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".