The density profile of clusters of galaxies : Abell 1351 & Abell 1995
Bibliographic record
Abstract
Two galaxy clusters, Abell 1351 and Abell 1995, are examined using weak gravitational lensing\ntechniques. Mass maps are created, and the clusters' density and shear profiles are compared to\ntheoretical predictions.\n\nGravitational lensing is important for studying the mass distribution of the Universe. It pro-\nvides methods of estimating the masses of everything acting as a gravitational lens, without \nmaking assumptions about the dynamical state and the nature of the gravitating matter. In \nparticular, this includes the dark matter, which is currently believed to make up for about \n90% of the entire matter in the Universe, evading any other means of direct detection.\nDark matter and its gravitation therefore govern the architecture and evolution of the largest\nbounds objects known in the Universe, from galaxies up to supercluster of galaxies.\n\nThis thesis uses data from the CFH12K wide-field imager at the Canada-France-Hawaii Telescope. \nIn order to obtain mass estimates for the galaxy clusters by weak lensing methods, distortions\nin the images of faint and distant background galaxies are measured. These distortions are \nintroduced into the images by the intervening tidal gravitational fields of the clusters along\nthe line of sight. Comparing the strength of the distortions to theoretical models, mass maps\nand density profiles of the lensing clusters are created.\n\nThe work includes standard image reduction techniques using the IMCAT software package. Details\nabout the astrometric calibration, shear measurements, point spread function (PSF) corrections \nand the method of mass distribution reconstruction are given, together with a description of \nthe principles of weak gravitational lensing.\n\nThe main results are that both clusters investigated contain a relatively large mass within the\nscale of their virial radius. Yet their density profiles are significantly different, in particular\nin their central parts. Similar results have been found in the recent years also for different \nclusters. The finding of this result was only possible due to the very large field of view\nof the CFH12K imager, which allowed to probe the shear field at very different angular scales.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".