THE ATACAMA COSMOLOGY TELESCOPE: THE LABOCA/ACT SURVEY OF CLUSTERS AT ALL REDSHIFTS
Bibliographic record
Abstract
We present a multi-wavelength analysis of 11 Sunyaev–Zel'dovich effect (SZE)-selected galaxy clusters (10 with new data) from the Atacama Cosmology Telescope (ACT) southern survey. We have obtained new imaging from the Large APEX Bolometer Camera (345 GHz; LABOCA) on the Atacama Pathfinder EXperiment (APEX) telescope, the Australia Telescope Compact Array (2.1 GHz; ATCA), and the Spectral and Photometric Imaging Receiver (250, 350, and 500 μ m; SPIRE) on the Herschel Space Observatory . 24 Spatially resolved 345 GHz SZE increments with integrated signal-to-noise ratio are found in six clusters. We compute 2.1 GHz number counts as a function of cluster-centric radius and find significant enhancements in the counts of bright sources at projected radii . By extrapolating in frequency, we predict that the combined signals from 2.1 GHz-selected radio sources and 345 GHz-selected submillimeter galaxies (SMGs) contaminate the 148 GHz SZE decrement signal by ∼5% and the 345 GHz SZE increment by ∼18%. After removing radio source and SMG emission from the SZE signals, we use ACT, LABOCA, and (in some cases) new Herschel SPIRE imaging to place constraints on the clusters' peculiar velocities. The sample's average peculiar velocity relative to the cosmic microwave background is .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".