X-RAY GROUPS OF GALAXIES IN THE AEGIS DEEP AND WIDE FIELDS
Bibliographic record
Abstract
We present the results of a search for extended X-ray sources and their corresponding galaxy groups from 800 ks Chandra coverage of the All-wavelength Extended Groth Strip International Survey (AEGIS). This yields one of the largest X-ray-selected galaxy group catalogs from a blind survey to date. The red-sequence technique and spectroscopic redshifts allow us to identify 100% of reliable sources, leading to a catalog of 52 galaxy groups. These groups span the redshift range z ∼ 0.066–1.544 and virial mass range M 200 ∼ 1.34 × 10 13 –1.33 × 10 14 M ☉ . For the 49 extended sources that lie within DEEP2 and DEEP3 Galaxy Redshift Survey coverage, we identify spectroscopic counterparts and determine velocity dispersions. We select member galaxies by applying different cuts along the line of sight or in projected spatial coordinates. A constant cut along the line of sight can cause a large scatter in scaling relations in low-mass or high-mass systems depending on the size of the cut. A velocity-dispersion-based virial radius can cause a larger overestimation of velocity dispersion in comparison to an X-ray-based virial radius for low-mass systems. There is no significant difference between these two radial cuts for more massive systems. Independent of radial cut, an overestimation of velocity dispersion can be created in the case of the existence of significant substructure and compactness in X-ray emission, which mostly occur in low-mass systems. We also present a comparison between X-ray galaxy groups and optical galaxy groups detected using the Voronoi–Delaunay method for DEEP2 data in this field.
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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 | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".