Searching for galaxy clusters using the aperture mass statistics in 50 VLT fields
Bibliographic record
Abstract
Application of the aperture mass (Map-) statistics provides a weak lensing method for the detection of cluster-sized dark matter halos. We present a new aperture filter function and maximise the effectiveness of the Map-statistics to detect cluster-sized halos using analytical models. We then use weak lensing mock catalogues generated from ray-tracing through N-body simulations, to analyse the effect of image treatment on the expected number density of halos. Using the Map-statistics, the aperture radius is typically several arcminutes, hence the aperture often lies partly outside a data field, consequently the signal-to-noise ratio of a halo detection decreases. We study these border effects analytically and by using mock catalogues. We find that the expected number density of halos decreases by a factor of two if the size of a field is comparable to the diameter of the aperture used. We finally report on the results of a weak lensing cluster search applying the Map-statistics to 50 randomly selected fields which were observed with FORS1 at the VLT. Altogether the 50 VLT fields cover an area of 0.64 square degrees. The I-band images were taken under excellent seeing conditions (average seeing ) which results in a high number density of galaxies used for the weak lensing analysis (). In five of the VLT fields, we detect a significant Map-signal which coincides with an overdensity of the light distribution. These detections are thus excellent candidates for shear-selected clusters.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".