How dark are filaments in the cosmic web?
Bibliographic record
Abstract
ABSTRACT The cold dark matter model predicts that dark matter haloes are connected by filaments. Direct measurements of the masses and structure of these filaments are difficult, but recently several studies have detected these dark-matter-dominated filaments using weak lensing. Here we study the efficiency of galaxy formation within the filaments by measuring their total mass-to-light ratios and stellar mass fractions. Specifically, we stack pairs of luminous red galaxies (LRGs) with a typical separation on the sky of 8 h−1 Mpc. We stack background galaxy shapes around pairs to obtain mass maps through weak lensing, and we stack galaxies from the Sloan Digital Sky Survey to obtain maps of light and stellar mass. To isolate the signal from the filament, we construct two matched catalogues of physical and non-physical (projected) LRG pairs, with the same distributions of redshift and separation. We then subtract the two stacked maps. Using LRG pair samples from the Baryon Oscillation Spectroscopic Survey at two different redshifts, we find that the evolution of the mass in filament is consistent with the predictions from perturbation theory. The filaments are not entirely dark: Their mass-to-light ratios (M/L = 351 ± 137 in solar units in the rband) and stellar mass fractions (Mstellar/M = 0.0073 ± 0.0030) are consistent with the cosmic values (and with their redshift evolutions).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".