Group-finding with photometric redshifts: the photo-z probability peaks algorithm
Bibliographic record
Abstract
We present a galaxy group-finding algorithm, the photo-z probability peaks (P3) algorithm, optimized for locating small galaxy groups using photometric redshift data by searching for peaks in the signal-to-noise ratio (S/N) of the local overdensity of galaxies in a 3D grid. This method is an improvement over similar 2D matched-filter methods in reducing background contamination through the use of redshift information, allowing it to accurately detect groups at lower richness. We present the results of tests of our algorithm on galaxy catalogues from the Millennium simulation. Using a minimum S/N of 3 for detected groups, a group aperture size of 0.25 h−1 Mpc, and assuming photometric redshift accuracy of σz= 0.05, it attains a purity of 84 per cent and detects ∼295 groups deg−2 with an average group richness of 8.6 members. Assuming photometric redshift accuracy of σz= 0.02, it attains a purity of 97 per cent and detects ∼143 groups deg−2 with an average group richness of 12.5 members. We also test our algorithm on data available for the COSMOS field and the presently available fields from the Canada–France–Hawaii Telescope Legacy Survey (CFHTLS)-Wide survey, presenting preliminary results of this analysis.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".