The Distance to Clusters: Correcting for Asphericity
Bibliographic record
Abstract
X-ray and Sunyaev-Zeldovich (SZ) effect observations can be combined to measure the distance to clusters of galaxies. The Hubble constant, H 0 , can be inferred from the distance to low-redshift clusters. With enough clusters to measure the redshift-distance relation out to a redshift z ~ 1, it may be possible to determine the total matter density, Ω 0 , and the cosmological constant, Λ 0 , as well. If the intracluster gas distribution is not spherical but elongated by a factor of Z along the line of sight, the inferred distance is increased by Z , and H 0 is decreased by the same factor. Averaging the inferred value of H 0 over a sufficiently large sample of clusters can reduce any systematic bias due to cluster shapes, provided the clusters are selected without any preferred orientation. Even so, elongation contributes significantly to the variance in the measured distances and in the inferred value of H 0 . With the addition of gravitational lensing observations, it is possible to infer the three-dimensional shape of an individual cluster, provided the gas is in hydrostatic equilibrium. We demonstrate a specific method for finding the shape and correcting the measured distances to individual clusters. To test this method, we apply it to artificial observations of idealized ellipsoidal model clusters. We base the artificial X-ray observations on the Chandra X-Ray Observatory . For the SZ effect, we assume modest improvements over current observations at the Owens Valley Radio Observatory. We recover the true distances to each of our model clusters without detectable bias and with statistical errors due to measurement uncertainties of 4%-6%.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".