The Impact of Halo Properties, Energy Feedback, and Projection Effects on the Mass‐SZ Flux Relation
Bibliographic record
Abstract
We present a detailed analysis of the intrinsic scatter in the integrated Sunyaev-Zel'dovich (SZ) effect-cluster mass ( Y − M ) relation, using semianalytic and simulated cluster samples. Specifically, we investigate the impact on the Y − M relation of energy feedback, variations in the host halo concentration and substructure populations, and projection effects due to unresolved clusters along the line of sight (the SZ background). Furthermore, we investigate at what radius (or overdensity) one should measure the integrated Sunyaev-Zel'dovich effect (SZE) and define cluster mass so as to achieve the tightest possible scaling. We find that the measure of Y with the least scatter is always obtained within a smaller radius than that at which the mass is defined; e.g., for M 200 ( M 500 ), the scatter is least for Y 500 ( Y 1100 ). The inclusion of energy feedback in the gas model significantly increases the intrinsic scatter in the Y − M relation due to larger variations in the gas mass fraction than in models without feedback. We also find that variations in halo concentration for clusters of a given mass explain why the integrated SZE provides a better mass proxy than the central decrement. Substructure is found to account for approximately 20% of the observed scatter in the Y − M relation. Above M 200 = 2 × 10 14 h −1 M ☉ , the SZ background does not significantly affect cluster mass measurements; below this mass, variations in the background signal reduce the optimal angular radius within which one should measure Y to achieve the tightest scaling with M 200 .
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 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".