Dust accretion and destruction in galaxy groups and clusters
Bibliographic record
Abstract
We examine the dust distribution around a sample of 70 000 low-redshift galaxy groups and clusters derived from the Sloan Digital Sky Survey (SDSS). By correlating spectroscopically identified background quasars with the galaxy groups, we obtain the relative colour excess due to dust reddening. We present a significant detection of dust out to a cluster-centric distance of 30 Mpc h−1 in all four independent SDSS colours, consistent with the expectations of weak-lensing masses of similar mass haloes and excess galaxy counts. The wavelength dependence of this colour excess is consistent with the expectations of a Milky Way dust law with RV= 3.1. Further, we find that the halo mass dependence of the dust content is much smaller than would be expected by a simple scaling, implying that the dust-to-gas ratio of the most massive clusters (∼1014h−1 M⊙) is ∼3 per cent of the local interstellar medium (ISM) value, while in small groups (∼1012.7h−1 M⊙) it is ∼55 per cent of the local ISM value. We also find that the dust must have a covering fraction of the order of 10 per cent to explain the observed colour differences, which means the dust is not just confined to the most massive galaxies. Comparing the dust profile with the excess galaxy profile, we find that the implied dust-to-galaxy ratio falls significantly towards the group or cluster centre. This has a significant halo mass dependence, such that the more massive groups and clusters show a stronger reduction. This suggests that either dust is destroyed by thermal sputtering of the dust grains by the hot, dense gas or the intrinsic dust production is reduced in these galaxies.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".