A New Galaxy Group Finding Algorithm: Probability Friends‐of‐Friends
Bibliographic record
Abstract
A new algorithm is developed, based on the friends-of-friends (FOF) algorithm, to identify galaxy groups in a galaxy catalog in which the redshift errors have large dispersions (e.g., a photometric redshift galaxy catalog in which a portion of the galaxies also have much more precise spectroscopic redshifts). The DEEP2 mock catalogs, with our additional simulated photometric redshift errors, are used to test the performance of our algorithm. The association of the reconstructed galaxy groups with the dark halos in the mock catalogs gives an idea about the completeness and purity of the derived group catalog. Our results show that in a 0.6 ⩽ z⩽ 1.6 galaxy catalog with an R-band limiting magnitude of 24.1 and an average 1 σ photometric redshift error of ~0.03, the overall purity of our new algorithm for richness 4-7 (line-of-sight velocity dispersion ~300 km s−1) groups is higher than 70% (i.e., 70% of the groups reconstructed by our algorithm are related to real galaxy groups). The performance of the new algorithm is compared with the performance of the FOF algorithm, and it is suggested that this new algorithm is better than FOF for a database, given the same redshift uncertainties.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 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".