Cosmology from LOFAR Two-metre Sky Survey Data Release 2: Cross-correlations with luminous red galaxies from eBOSS
Bibliographic record
Abstract
We cross-correlated galaxies from the LOw-Frequency ARray (LOFAR) Two-metre Sky Survey (LoTSS) second data release (DR2) radio source with the extended Baryon Oscillation Spectroscopic Survey (eBOSS) luminous red galaxy (LRG) sample to extract the baryon acoustic oscillation (BAO) signal and constrain the linear clustering bias of radio sources in LoTSS DR2. In the LoTSS DR2 catalogue, employing a flux density limit of $1.5$ mJy at the central LoTSS frequency of 144 MHz and a signal-to-noise ratio (S/N) of $7.5$, additionally considering eBOSS LRGs with redshifts between 0.6 and 1, we measured both the angular LoTSS-eBOSS cross-power spectrum and the angular eBOSS auto-power spectrum. These measurements were performed across various eBOSS redshift tomographic bins with a width of $Δz=0.06$. By marginalising over the broadband shape of the angular power spectra, we searched for a BAO signal in cross-correlation with radio galaxies, and determine the linear clustering bias of LoTSS radio sources for a constant-bias and an evolving-bias model. Using the cross-correlation, we measured the isotropic BAO dilation parameter as $α=1.01\pm 0.11$ at $z_{\rm eff}=0.63$. By combining four redshift slices at $z_{\rm eff}=0.63, 0.69, 0.75$, and $0.81$, we determined a more constrained value of $α= 0.968^{+0.060}_{-0.095}$. For the entire redshift range of $z_{\rm eff}=0.715$, we measured $b_C = 2.64 \pm 0.20$ for the constant-bias model, $b(z)=b_C$, and then $b_D = 1.80 \pm 0.13$ for the evolving-bias model, $b(z) = b_D / D(z)$, with $D(z)$ denoting the growth rate of linear structures. Additionally, we measured the clustering bias for individual redshift bins.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.005 | 0.002 |
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".