"SZ spectroscopy" in the coming decade: Galaxy cluster cosmology and astrophysics in the submillimeter
Bibliographic record
Abstract
Sunyaev-Zeldovich (SZ) effects were first proposed in the 1970s as tools to identify the X-ray emitting hot gas inside massive clusters of galaxies and obtain their velocities relative to the cosmic microwave background (CMB). Yet it is only within the last decade that they have begun to significantly impact astronomical research. Thanks to the rapid developments in CMB instrumentation, measurement of the dominant thermal signature of the SZ effects has become a routine tool to find and characterize large samples of galaxy clusters and to seek deeper understanding of several important astrophysical processes via high-resolution imaging studies of many targets. With the notable exception of the Planck satellite and a few combinations of ground-based observatories, much of this "SZ revolution" has happened in the photometric mode, where observations are made at one or two frequencies in the millimeter regime to maximize the cluster detection significance and minimize the foregrounds. Still, there is much more to learn from detailed and systematic analyses of the SZ spectra across multiple wavelengths, specifically in the submillimeter (>300 GHz) domain. The goal of this Science White Paper is to highlight this particular aspect of SZ research, point out what new and potentially groundbreaking insights can be obtained from these studies, and emphasize why the coming decade can be a golden era for SZ spectral measurements.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".