Detecting clusters and groups of galaxies populating the local Universe in large optical spectroscopic surveys
Bibliographic record
Abstract
Context. With the advent of wide-field cosmological surveys, samples of hundreds of thousands of spectroscopically confirmed galaxy groups and clusters are becoming available. While these large datasets offer a valuable tool to trace the baryonic matter distribution, controlling systematics in the identification of host dark-matter halos and estimating their properties remains crucial. Aims. We intend to evaluate the predictions of retrieving the population of cluster and group of galaxies using three group-detection methods on a simulated dataset replicating the GAMA survey selection. Our goal is to understand the systematics and selection effects of each group finder, which will be instrumental for interpreting the unprecedented volume of spectroscopic data from SDSS, GAMA, DESI, and WAVES, and for leveraging optical catalogues in the (X-ray) eROSITA era to quantify the baryonic mass in galaxy groups. Methods. We simulated a spectroscopic galaxy survey in the local Universe (down to z < 0.2 and stellar mass completeness M ⋆ ≥ 10 9.8 M ⊙ ) using a lightcone based on the cosmological hydrodynamical simulation Magneticum. We assessed the completeness and contamination levels of the reconstructed halo catalogues and analysed the reconstructed membership. Finally, we evaluated the halo-mass recovery rate of the group finders and explored potential improvements. Results. All three group finders demonstrate high completeness levels (> 80%) on the galaxy group and cluster scales, confirming that optical selection is suitable for probing dense regions in the Universe. Contamination at the low-mass end ( M 200 < 10 13 M ⊙ ) is caused by interlopers and fragmentation. Galaxy membership is at least 70% accurate above the group-mass scale; however, inaccuracies can lead to systematic biases in halo-mass determination using the velocity dispersion of galaxy members. We recommend using other halo-mass proxies less affected by contamination – such as total stellar luminosity or mass – to recover accurate halo masses. Further analysis of the cumulative luminosity function of the galaxy members has shown remarkable accuracy in the group finders’ predictions of the galaxy population. Conclusions. These results confirm the reliability and completeness of the spectroscopic catalogues compiled by these state-of-the-art group finders. This paves the way for studies that require large sets of spectroscopically confirmed galaxy groups and clusters or studies of galaxy evolution in different environments.
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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 | 0.000 |
| Open science | 0.000 | 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".