Bibliographic record
Abstract
Sunyaev-Zel'dovich (SZ) cluster surveys are considered among the most promising methods for probing dark energy up to large redshifts. However, their premise is hinged on an accurate mass-observable relationship, which could be affected by the (rather poorly understood) physics of the intracluster gas. In this paper, using a semianalytic model of the intracluster gas that accommodates various theoretical uncertainties, I develop a fundamental plane relationship between the observed size, thermal energy, and mass of galaxy clusters. In particular, I find that M ∝ ( Y SZ / R SZ ,2 ) 3/4 , where M is the mass, Y SZ is the total SZ flux or thermal energy, and R SZ ,2 is the SZ half-light radius of the cluster. I first show that, within this model, using the fundamental plane relationship reduces the (systematic+random) errors in mass estimates to 14%, from 22% for a simple mass-flux relationship. Since measurement of the cluster sizes is an inevitable part of observing the SZ clusters, the fundamental plane relationship can be used to reduce the error of the cluster mass estimates by ~34%, improving the accuracy of the resulting cosmological constraints without any extra cost. I then argue why our fundamental plane is distinctly different from the virial relationship that one may naively expect between the cluster parameters. Finally, I argue that while including more details of the observed SZ profile cannot significantly improve the accuracy of mass estimates, a better understanding of the impact of nongravitational heating/cooling processes on the outskirts of the intracluster medium (apart from external calibrations) might be the best way to reduce these errors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".