FINDING GALAXY GROUPS IN PHOTOMETRIC-REDSHIFT SPACE: THE PROBABILITY FRIENDS-OF-FRIENDS ALGORITHM
Bibliographic record
Abstract
We present a structure-finding algorithm designed to identify galaxy groups in photometric-redshift data sets: the probability friends-of-friends (pFoF) algorithm. This algorithm is derived by combining the FoF algorithm in the transverse direction and the photometric-redshift probability densities in the radial dimension. The innovative characteristic of our group-finding algorithm is the improvement of redshift estimation via the constraints given by the transversely connected galaxies in a group, based on the assumption that all galaxies in a group have the same redshift. Tests using the Virgo Consortium Millennium Simulation mock catalogs allow us to show that the recovery rate of the pFoF algorithm is larger than 80% for mock groups of at least 2 × 10 13 M ☉ , while the false detection rate is about 10% for pFoF groups containing around at least eight net members. Applying the algorithm to the Canadian Network for Observational Cosmology Survey group catalogs gives results which are consistent with the mock catalog tests. From all these results, we conclude that our group-finding algorithm offers an effective yet simple way to identify galaxy groups in photometric-redshift catalogs.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".