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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".