Self Calibration in Cluster Studies of Dark Energy: Combining the Cluster Redshift Distribution, the Power Spectrum and Mass Measurements
Bibliographic record
Abstract
We examine the prospects for measuring the dark energy equation of state parameter w within the context of any uncertain redshift evolution of galaxy cluster structure (building on Majumdar and Mohr, 2003) and show that including the redshift averaged cluster power spectrum, P_cl(k), and direct mass measurements of 100 clusters helps tremendously in reducing cosmological parameter uncertainties. Specifically, we show that when combining the redshift distribution and the power spectrum information for a particular X-ray survey (DUET) and two SZE surveys (SPT & Planck), the constraints on the dark energy equation of state w can be improved by roughly a factor of 4. Because surveys designed to study the redshift distribution of clusters will have all the information necessary to construct P_cl(k), the benefit of adding P_cl(k) in reducing uncertainties comes at no additional observational cost. Combining detailed mass studies of 100 clusters with the redshift distribution improves the parameter uncertainties by a factor of 3-5. The data required for these detailed mass measurements-- assumed to have 1sigma uncertainties of 30-- are accumulating in the the XMM-Newton and Chandra archives. The best constraints are obtained when one combines both the power spectrum constraints and mass measurements with the cluster redshift distribution; when using the survey to extract the parameters and evolution of the mass--observable relations, we estimate the uncertainties on w of ~4% to 6%. These parameter constraints are obtained from self-calibrating cluster surveys alone. In combination with CMB or distance measurements that have different parameter degeneracies, cluster studies of dark energy will provide enhanced constraints and allow for cross--checks of systematics.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".