Correlations in the Cosmic Far-infrared Background at 250, 350, and 500 μm Reveal Clustering of Star-forming Galaxies
Bibliographic record
Abstract
We demonstrate the application of CMB techniques to measure the clustering of infrared emitting star-forming galaxies. We detect correlations in the cosmic far-infrared background due to the clustering of star-forming galaxies in observations made with the Balloon-borne\nLarge Aperture Submillimeter Telescope, BLAST, at 250, 350, and 500μm. We perform\njackknife and other tests to confirm the reality of the signal. The measured correlations are well fit by a power law over scales of 5–25 arcminutes, with ∆I/I = 15.1 ± 1.7%. We adopt a specific model for submillimeter sources in which the contribution to clustering comes from sources in the redshift ranges 1.3≤z≤2.2, 1.5≤z≤2.7,and1.7≤z≤3.2,at 250, 350 and 500 μm, respectively. With these distributions, our measurement of the power spectrum, P(kθ), corresponds to linear bias parameters, b = 3.8±0.6,3.9±0.6 and 4.4±0.7,\nrespectively. We further interpret the results in terms of the halo model, and find that at the smaller scales, the simplest halo model fails to fit our results. One way to improve the fit is to increase the radius at which dark matter halos are artificially truncated in the model, which is equivalent to having some star-forming galaxies at z ≥ 1 located in the outskirts of groups and clusters. In the context of this model we find a minimum halo mass required to host a\ngalaxy is log(Mmin/M⊙) = 11.5+0.4, and we derive effective biases beff = 2.2 ± 0.2, 2.4 ± 0.2, −0.1 and 2.6 ± 0.2, and effective masses log(Meff/M⊙) = 12.9 ± 0.3, 12.8 ± 0.2, and 12.7 ± 0.2 , at 250, 350 and 500 μm, corresponding to spatial correlation lengths of r0 = 4.9, 5.0, and 5.2 ±0.7 h−1 Mpc, respectively. Finally, we discuss implications for clustering measurement strategies with Herschel and Planck.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".