The matter distribution in the local Universe as derived from galaxy groups in SDSS DR12 and 2MRS
Bibliographic record
Abstract
Context. Friends-of-friends algorithms are a common tool to detect galaxy groups and clusters in large survey data. In order to be as precise as possible, they have to be carefully calibrated using mock catalogues. Aims. We create an accurate and robust description of the matter distribution in the local Universe using the most up-to-date available data. This will provide the input for a specific cosmological test planned as follow-up to this work, and will be useful for general extragalactic and cosmological research. Methods. We created a set of galaxy group catalogues based on the 2MRS and SDSS DR12 galaxy samples using a friends-of-friends based group finder algorithm. The algorithm was carefully calibrated and optimised on a new set of wide-angle mock catalogues from the Millennium simulation, in order to provide accurate total mass estimates of the galaxy groups taking into account the relevant observational biases in 2MRS and SDSS. Results. We provide four different catalogues: (i) a 2MRS based group catalogue; (ii) an SDSS DR12 based group catalogue reaching out to a redshift z = 0.11 with stellar mass estimates for 70% of the galaxies; (iii) a catalogue providing additional fundamental plane distances for all groups of the SDSS catalogue that host elliptical galaxies; (iv) a catalogue of the mass distribution in the local Universe based on a combination of our 2MRS and SDSS catalogues. Conclusions. While motivated by a specific cosmological test, three of the four catalogues that we produced are well suited to act as reference databases for a variety of extragalactic and cosmological science cases. Our catalogue of fundamental plane distances for SDSS groups provides further added value to this paper.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".