Cosmic star formation history with tomographic cosmic infrared background-galaxy cross-correlation
Bibliographic record
Abstract
In this work we present a new method for probing the star formation history of the Universe, namely tomographic cross-correlation between the cosmic infrared background (CIB) and galaxy samples. The galaxy samples are from the Kilo-Degree Survey (KiDS), while the CIB maps are made from Planck sky maps at 353, 545, and 857 GHz. We measure the cross-correlation in harmonic space within 100 < ℓ < 2000 with a significance of 43σ. We model the cross-correlation with a halo model, which links CIB anisotropies to star formation rates (SFRs) and galaxy abundance. We assume that the SFR has a lognormal dependence on halo mass and that the galaxy abundance follows the halo occupation distribution (HOD) model. The cross-correlations give a best-fit maximum star formation efficiency of ηmax = 0.41−0.14+0.09 at a halo mass log10(Mpeak/M⊙) = 12.14 ± 0.36. The derived star formation rate density (SFRD) is well constrained up to z ∼ 1.5. The constraining power at high redshift is mainly limited by the KiDS survey depth. We also show that the constraint is robust to uncertainties in the estimated redshift distributions of the galaxy sample. A combination with external SFRD measurements from previous studies gives log10(Mpeak/M⊙) = 12.42−0.19+0.35. This tightens the SFRD constraint up to z = 4, yielding a peak SFRD of 0.09−0.004+0.003 M⊙ yr−1 Mpc−3 at z = 1.74−0.02+0.06, corresponding to a lookback time of 10.05−0.03+0.12 Gyr. Both constraints are consistent, and the derived SFRD agrees with previous studies and simulations. This validates the use of CIB tomography as an independent probe of the star formation history of the Universe. Additionally, we estimate the galaxy bias, b, of KiDS galaxies from the constrained HOD parameters and obtain an increasing bias from b = 1.1−0.31+0.17 at z = 0 to b = 1.96−0.64+0.18 at z = 1.5, which highlights the potential of this method as a probe of galaxy abundance. Finally, we provide a forecast for future galaxy surveys and conclude that, due to their considerable depth, future surveys will yield a much tighter constraint on the evolution of the SFRD.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.002 | 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".