Nessie: A rust-powered, fast, flexible, and generalized friends-of-friends galaxy-group finder in R and Python
Bibliographic record
Abstract
We introduce Nessie , a galaxy group finder implemented in Rust and distributed as both a Python and R package. Nessie employs the friends-of-friends (FoF) algorithm and requires only on-sky position and redshift as input, making it immediately applicable to surveys that lack a well-defined luminosity function. We implement several algorithmic optimizations – including binary search and k-d tree pre-selection – that significantly improve performance by reducing unnecessary galaxy pair checks. To validate the accuracy of Nessie , we tune its parameters using a suite of GALFORM mock lightcones and achieve a strong Figure of Merit. We further demonstrate its reliability by applying it to both the GAMA and SDSS surveys, where it produces group catalogues consistent with those in the literature. Additional functionality is included for comparison with simulations and mock catalogues. Benchmarking on a standard MacBook Pro (M3 chip with 11 cores) shows that version 1 of Nessie can process ∼ 1 million galaxies in ∼ 10 s, highlighting its speed and suitability for next-generation redshift surveys.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.043 | 0.055 |
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".