LoCuSS: the near-infrared luminosity and weak-lensing mass scaling relation of galaxy clusters
Bibliographic record
Abstract
We present the first scaling relation between weak-lensing galaxy cluster mass, MWL, and near-infrared luminosity, LK. Our results are based on 17 clusters observed with wide-field instruments on Subaru, the United Kingdom Infrared Telescope, the Mayall Telescope, and the MMT. We concentrate on the relation between projected 2D weak-lensing mass and spectroscopically confirmed luminosity within 1 Mpc, modelled as |$M_{\rm WL} \propto L_{K}^b$|, obtaining a power-law slope of |$b=0.83^{+0.27}_{-0.24}$| and an intrinsic scatter of |$\sigma _{lnM_{\rm WL}|L_{K}}=10^{+8}_{-5}$| per cent. Intrinsic scatter of ∼10 per cent is a consistent feature of our results regardless of how we modify our approach to measuring the relationship between mass and light. For example, deprojecting the mass and measuring both quantities within r500, that is itself obtained from the lensing analysis, yields |$\sigma _{lnM_{\rm WL}|L_{K}}=10^{+7}_{-5}$| per cent and |$b=0.97^{+0.17}_{-0.17}$|. We also find that selecting members based on their (J − K) colours instead of spectroscopic redshifts neither increases the scatter nor modifies the slope. Overall our results indicate that near-infrared luminosity measured on scales comparable with r500 (typically 1 Mpc for our sample) is a low scatter and relatively inexpensive proxy for weak-lensing mass. Near-infrared luminosity may therefore be a useful mass proxy for cluster cosmology experiments.
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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 | 0.006 |
| 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 | 0.000 | 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".