Weak-lensing mass estimates of galaxy groups and the line-of-sight contamination
Bibliographic record
Abstract
Weak lensing is an important technique to determine the masses of galaxy groups. However, the distortion imprint on the shape of the background galaxies is not only affected by the gravitational field of the main group, but also affected by all the mass content along the line of sight. Using COSMOS shear mock data, we study the shear profile around 165 groups and investigate the level at which the neighbouring groups can enhance or suppress the shear signal from the main halo. The mock data are based on Canada–France–Hawaii Telescope and Subaru observations, which are used to obtain the photometric redshifts of galaxies in the field and a realistic galaxy density, given by the weak-lensing distortion analysis of the observed data. We further use information on the galaxy groups (having a median mass and redshift of M200= 3.1 × 1013 M⊙ and z = 0.68, respectively) from the COSMOS X-ray catalogue of extended sources. The expected gravitational shear field of these groups is calculated assuming that the haloes are described by Navarro–Frenk–White density profiles, and the total shear is computed by summing the shear over all the lenses. We conclude that, on average, the signal-to-noise ratio for a detection of the main halo is affected by with respect to the signal-to-noise ratio the same halo would have if it was isolated in the sky. Groups with neighbours that are close in projected distance (≲1 arcmin) are the most affected, but haloes located at larger angular distances also cause a measurable shear signal. These (angular) distant groups can be interpreted as uncorrelated large-scale structure. The average bias in the mass-excess estimate of individual groups that is introduced by the external haloes is zero with an rms of ∼6–72 per cent, depending on the aperture size used. One way to eliminate this bias is by stacking the density profile of several groups. The shear signal introduced by large-scale structure acts as an external source of noise. The averaged uncertainty introduced is per component for an aperture size of θ∼ 5 arcmin, which corresponds to ∼1.8 per cent of the one-component intrinsic ellipticity value. This large-scale structure noise error becomes equal to intrinsic ellipticity noise if there are measurements for ∼3000 galaxies within a certain aperture, a number that is already achieved by current deep surveys such as COSMOS and therefore that should not be ignored.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".