Cross-correlation of Planck CMB lensing and CFHTLenS galaxy weak lensing maps
Bibliographic record
Abstract
We cross-correlate cosmic microwave background (CMB) lensing and galaxy weak lensing maps using the Planck 2013 and 2015 data and the $154\text{ }\text{ }{\mathrm{deg}}^{2}$ Canada-France-Hawaii Telescope Lensing Survey (CFHTLenS). This measurement probes large-scale structure at intermediate redshifts $\ensuremath{\approx}0.9$, between the high- and low-redshift peaks of the CMB and CFHTLenS lensing kernels, respectively. Using the noise properties of these data sets and standard Planck 2015 $\mathrm{\ensuremath{\Lambda}}\mathrm{CDM}$ cosmological parameters, we forecast a signal-to-noise ratio $\ensuremath{\approx}4.6$ for the cross-correlation. We find that the noise level of our actual measurement agrees well with this estimate, but the amplitude of the signal lies well below the theoretical prediction. The best-fit amplitudes of our measured cross-correlations are ${A}_{2013}=0.48\ifmmode\pm\else\textpm\fi{}0.26$ and ${A}_{2015}=0.44\ifmmode\pm\else\textpm\fi{}0.22$, using the 2013 and 2015 Planck CMB lensing maps, respectively, where $A=1$ corresponds to the fiducial Planck 2015 $\mathrm{\ensuremath{\Lambda}}\mathrm{CDM}$ prediction. Due to the low measured amplitude, the detection significance is moderate ($\ensuremath{\approx}2\ensuremath{\sigma}$) and the data are in tension with the theoretical prediction $(\ensuremath{\approx}2--2.5\ensuremath{\sigma})$. The tension is reduced somewhat when compared to predictions using WMAP9 parameters, for which we find ${A}_{2013}=0.56\ifmmode\pm\else\textpm\fi{}0.30$ and ${A}_{2015}=0.52\ifmmode\pm\else\textpm\fi{}0.26$. We consider various systematic effects, finding that photometric redshift uncertainties, contamination by intrinsic alignments, and effects due to the masking of galaxy clusters in the Planck 2015 CMB lensing reconstruction are able to help resolve the tension at a significant level ($\ensuremath{\approx}10%$ each). An overall multiplicative bias in the CFHTLenS shear data could also play a role, which can be tested with existing data. We close with forecasts for measurements of the CMB lensing---galaxy lensing cross-correlation using ongoing and future weak lensing surveys, which will definitively test the significance of the tension in our results with respect to $\mathrm{\ensuremath{\Lambda}}\mathrm{CDM}$.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".