CFHTLenS: higher order galaxy–mass correlations probed by galaxy–galaxy–galaxy lensing
Bibliographic record
Abstract
We present the first direct measurement of the galaxy–matter bispectrum as a function of galaxy luminosity, stellar mass and type of spectral energy distribution (SED). Our analysis uses a galaxy–galaxy–galaxy lensing technique (G3L), on angular scales between 9 arcsec and 50 arcmin, to quantify (i) the excess surface mass density around galaxy pairs (excess mass hereafter) and (ii) the excess shear–shear correlations around single galaxies, both of which yield a measure of two types of galaxy–matter bispectra. We apply our method to the state-of-the-art Canada–France–Hawaii Telescope Lensing Survey (CFHTLenS), spanning 154 square degrees. This survey allows us to detect a significant change of the bispectra with lens properties. Measurements for lens populations with distinct redshift distributions become comparable by a newly devised normalization technique. That will also aid future comparisons to other surveys or simulations. A significant dependence of the normalized G3L statistics on luminosity within −23 ≤ Mr ≤ −18 and stellar mass within 5 × 109 M⊙ ≤ M* ≤ 2 × 1011 M⊙ is found (h = 0.73). Both bispectra exhibit a stronger signal for more luminous lenses or those with higher stellar mass (up to a factor of 2–3). This is accompanied by a steeper equilateral bispectrum for more luminous or higher stellar mass lenses for the excess mass. Importantly, we find the excess mass to be very sensitive to galaxy type as recently predicted with semi-analytic galaxy models: luminous (Mr < −21) late-type galaxies show no detectable signal, while all excess mass detected for luminous galaxies seems to be associated with early-type galaxies. We also present the first observational constraints on third-order stochastic galaxy biasing parameters.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".