CROSS-CORRELATION BETWEEN THE CMB LENSING POTENTIAL MEASURED BY<i>PLANCK</i>AND HIGH-<i>z</i>SUBMILLIMETER GALAXIES DETECTED BY THE<i>HERSCHEL</i>-ATLAS SURVEY
Bibliographic record
Abstract
We present the first measurement of the correlation between the map of the cosmic microwave background (CMB) lensing potential derived from the Planck nominal mission data and galaxies detected by the Herschel -ATLAS ( H -ATLAS) survey covering about , i.e., about 1.4% of the sky. We reject the hypothesis that there is no correlation between CMB lensing and galaxy detection at a significance, checking the result by performing a number of null tests. The significance of the detection of the theoretically expected cross-correlation signal is found to be . The galaxy bias parameter, b , derived from a joint analysis of the cross-power spectrum and of the autopower spectrum of the galaxy density contrast is found to be , consistent with earlier estimates for H -ATLAS galaxies at similar redshifts. On the other hand, the amplitude of the cross-correlation is found to be a factor 1.62 ± 0.16 higher than expected from the standard model and also found by cross-correlation analyses with other tracers of the large-scale structure. The enhancement due to lensing magnification can account for only a fraction of the excess cross-correlation signal. We suggest that part of it may be due to an incomplete removal of the contamination of the cosmic infrared background, which includes the H -ATLAS sources we are cross-correlating with. In any case, the highly significant detection reported here using a catalog covering only 1.4% of the sky demonstrates the potential of CMB lensing correlations with submillimeter surveys.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".