Cosmic filaments in galaxy cluster outskirts: quantifying finding filaments in redshift space
Bibliographic record
Abstract
ABSTRACT Inferring line-of-sight distances from redshifts in and around galaxy clusters is complicated by peculiar velocities, a phenomenon known as the ‘Fingers of God’ (FoG). This presents a significant challenge for finding filaments in large observational data sets as these artificial elongations can be wrongly identified as cosmic web filaments by extraction algorithms. Upcoming targeted wide-field spectroscopic surveys of galaxy clusters and their infall regions, such as the WEAVE Wide-Field Cluster Survey, motivate our investigation of the impact of FoG on finding filaments connected to clusters. Using zoom-in resimulations of 324 massive galaxy clusters and their outskirts from the three hundred project, we test methods typically applied to large-scale spectroscopic data sets. This paper describes our investigation of whether a statistical compression of the FoG of cluster centres and galaxy groups can lead to correct filament extractions in the cluster outskirts. We find that within 5R200 (∼15 h−1 Mpc) statistically correcting for FoG elongations of virialized regions does not achieve reliable filament networks compared to reference filament networks based on true positions. This is due to the complex flowing motions of galaxies towards filaments in addition to the cluster infall, which overwhelm the signal of the filaments relative to the volume that we probe. While information from spectroscopic redshifts is still important to isolate the cluster regions, and thereby reduce background and foreground interlopers, we expect future spectroscopic surveys of galaxy cluster outskirts to rely on 2D positions of galaxies to extract cosmic filaments.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 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) | 0.001 | 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 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".