Probing the missing baryons with the Sunyaev-Zel’dovich effect from filaments
Bibliographic record
Abstract
Observations of galaxies and galaxy clusters in the local universe can account for only ∼10% of the total baryon content. Cosmological simulations predict that the “missing baryons” are spread throughout filamentary structures in the cosmic web, forming a low-density gas with temperatures of 105−107 K. We search for this warm-hot intergalactic medium (WHIM) by stacking the Planck Compton y-parameter map of the thermal Sunyaev-Zel’dovich (tSZ) effect for 1 002 334 pairs of CMASS galaxies from the Sloan Digital Sky Survey. We model the contribution from the galaxy halo pairs assuming spherical symmetry, finding a residual tSZ signal at the 2.9σ level from a stacked filament of length 10.5 h−1 Mpc with a Compton parameter magnitude y = (0.6 ± 0.2)×10−8. We consider possible sources of contamination and conclude that bound gas in haloes may contribute only up to 20% of the measured filamentary signal. To estimate the filament gas properties we measure the gravitational lensing signal for the same sample of galaxy pairs; in combination with the tSZ signal, this yields an inferred gas density of ρb = (5.5 ± 2.9) × ρ̄b with a temperature T = (2.7 ± 1.7) × 106 K. This result is consistent with the predicted WHIM properties, and overall the filamentary gas can account for 11 ± 7% of the total baryon content of the Universe. We also see evidence that the gas filament extends beyond the galaxy pair. Averaging over this longer baseline boosts the significance of the tSZ signal and increases the associated baryon content to 28 ± 12% of the global value.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".