The Atacama Cosmology Telescope: measuring radio galaxy bias through cross-correlation with lensing
Bibliographic record
Abstract
We correlate the positions of radio galaxies in the FIRST survey with the cosmic microwave background lensing convergence estimated from the Atacama Cosmology Telescope over 470 deg2 to determine the bias of these galaxies. We remove optically cross-matched sources below redshift z = 0.2 to preferentially select active galactic nuclei (AGN). We measure the angular cross-power spectrum |$C_l^{\kappa g}$| at 4.4σ significance in the multipole range 100 < l < 3000, corresponding to physical scales within ≈2–60 Mpc at an effective redshift zeff = 1.5. Modelling the AGN population with a redshift-dependent bias, the cross-spectrum is well fitted by the Planck best-fitting Λ cold dark matter cosmological model. Fixing the cosmology and assumed redshift distribution of sources, we fit for the overall bias model normalization, finding b(zeff) = 3.5 ± 0.8 for the full galaxy sample and b(zeff) = 4.0 ± 1.1(3.0 ± 1.1) for sources brighter (fainter) than 2.5 mJy. This measurement characterizes the typical halo mass of radio-loud AGN: we find |$\log (M_{\rm halo} / \,\mathrm{M}_{\odot }) = 13.6^{+0.3}_{-0.4}$|.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".